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  • 1
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    Decisions in economics and finance 16 (1993), S. 73-86 
    ISSN: 1129-6569
    Keywords: project analysis ; linear programming ; internal financial law (IFL) ; financial leverage ; discounted cash-flows (DCF) decomposition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Description / Table of Contents: Riassunto Si definisce un modello generale (PAULA) per la valutazione, selezione e gestione ottimale di progetti certi alternativi. Sfruttando i risvolti formali e finanziari dei problemi lineari associati (diretto e duale), si formulano poi due proposte per definire una legge finanziaria interna (IFL) ottimale, utili sia per abbattere la molteplicità intrinseca delleIFL, sia per evitare risultati economicamente arbitrari nel loro uso.
    Notes: Abstract We define a general model (called PAULA) for the valuation, optimal management and selection among mutually exclusive safe projects. By exploiting the formal and financial features of the associated linear problems (primal and dual), we put forward two proposals to define an optimal internal financial law (IFL). They may be used to reduce the multiplicity of the IFLs and to avoid economically arbitrary outcomes.
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  • 2
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    Mathematical programming 47 (1990), S. 175-201 
    ISSN: 1436-4646
    Keywords: Optimization ; linear programming ; complexity ; polynomial time algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We present an algorithm for linear programming which requires O(((m+n)n 2+(m+n)1.5 n)L) arithmetic operations wherem is the number of constraints, andn is the number of variables. Each operation is performed to a precision of O(L) bits.L is bounded by the number of bits in the input. The worst-case running time of the algorithm is better than that of Karmarkar's algorithm by a factor of $$\sqrt {m + n} $$ .
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  • 3
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    Mathematical programming 49 (1990), S. 91-111 
    ISSN: 1436-4646
    Keywords: Sparse matrices ; linear programming ; bipartite matching
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Many optimization algorithms involve repeated processing of a fixed set of linear constraints. If we pre-process the constraint matrixA to be sparser, then algebraic operations onA will become faster. We consider the problem of making a given matrix as sparse as possible, theSparsity Problem (SP). In a companion paper with S. Frank Chang, we developed some theoretical algorithms for SP under a non-degeneracy assumption (McCormick and Chang, 1988). Here we investigate what must be done to make those algorithms applicable in practice. We report encouraging computational results in making linear programming constraint matrices sparser. We also find that the Simplex Algorithm can solve the reduced LPs faster. Comparisons are made to a heuristic algorithm for SP of Adler et al. (1989).
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  • 4
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    Mathematical programming 52 (1991), S. 209-225 
    ISSN: 1436-4646
    Keywords: Karmarkar's algorithm ; linear programming ; projective algorithm ; conical projection ; interior methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Interior methods for linear programming were designed mainly for problems formulated with equality constraints and non-negative variables. The formulation with inequality constraints has shown to be very convenient for practical implementations, and the translation of methods designed for one formulation into the other is not trivial. This paper relates the geometric features of both representations, shows how to transport data and procedures between them and shows how cones and conical projections can be associated with inequality constraints.
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  • 5
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    Mathematical programming 54 (1992), S. 267-279 
    ISSN: 1436-4646
    Keywords: Linear complementarity ; P-matrix ; interior point ; potential function ; linear programming ; quadratic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The linear complementarity problem (LCP) can be viewed as the problem of minimizingx T y subject toy=Mx+q andx, y⩾0. We are interested in finding a point withx T y 〈ε for a givenε 〉 0. The algorithm proceeds by iteratively reducing the potential function $$f(x,y) = \rho \ln x^T y - \Sigma \ln x_j y_j ,$$ where, for example,ρ=2n. The direction of movement in the original space can be viewed as follows. First, apply alinear scaling transformation to make the coordinates of the current point all equal to 1. Take a gradient step in the transformed space using the gradient of the transformed potential function, where the step size is either predetermined by the algorithm or decided by line search to minimize the value of the potential. Finally, map the point back to the original space. A bound on the worst-case performance of the algorithm depends on the parameterλ *=λ*(M, ε), which is defined as the minimum of the smallest eigenvalue of a matrix of the form $$(I + Y^{ - 1} MX)(I + M^T Y^{ - 2} MX)^{ - 1} (I + XM^T Y^{ - 1} )$$ whereX andY vary over the nonnegative diagonal matrices such thate T XYe ⩾ε andX jj Y jj⩽n 2. IfM is a P-matrix,λ * is positive and the algorithm solves the problem in polynomial time in terms of the input size, |log ε|, and 1/λ *. It is also shown that whenM is positive semi-definite, the choice ofρ = 2n+ $$\sqrt {2n} $$ yields a polynomial-time algorithm. This covers the convex quadratic minimization problem.
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  • 6
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    Mathematical programming 40 (1988), S. 197-204 
    ISSN: 1436-4646
    Keywords: Greedy algorithms ; series parallel graphs ; linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This note describes some sufficient conditions for the maximum or minimum of a weighted flow (the weights are on paths, and are derived from weights on the edges of the path), of given volume in a series parallel graph to be found by a greedy algorithm.
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  • 7
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    Mathematical programming 35 (1986), S. 193-224 
    ISSN: 1436-4646
    Keywords: Local improvement ; average performance of algorithms ; linear complementarity ; linear programming ; extremal set theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We present a general abstract model of local improvement, applicable to such diverse cases as principal pivoting methods for the linear complementarity problem and hill climbing in artificial intelligence. The model accurately predicts the behavior of the algorithms, and allows for a variety of probabilistic assumptions that permit degeneracy. Simulation indicates an approximately linear average number of iterations under a variety of probability assumptions. We derive theoretical bounds of 2en logn and en 2 for different distributions, respectively, as well as polynomial bounds for a broad class of probability distributions. We conclude with a discussion of the applications of the model to LCP and linear programming.
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  • 8
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    Mathematical programming 60 (1993), S. 1-19 
    ISSN: 1436-4646
    Keywords: Convex programming ; linear programming ; multiplier method ; exponential penalty ; Augmented Lagrangian
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper, we analyze the exponential method of multipliers for convex constrained minimization problems, which operates like the usual Augmented Lagrangian method, except that it uses an exponential penalty function in place of the usual quadratic. We also analyze a dual counterpart, the entropy minimization algorithm, which operates like the proximal minimization algorithm, except that it uses a logarithmic/entropy “proximal” term in place of a quadratic. We strengthen substantially the available convergence results for these methods, and we derive the convergence rate of these methods when applied to linear programs.
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  • 9
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    Mathematical programming 60 (1993), S. 215-228 
    ISSN: 1436-4646
    Keywords: Primary 90C05 ; 90C33 ; Strict complementarity ; maximal complementarity ; interior point algorithms ; linear programming ; monotone complementarity problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We show that most interior-point algorithms for linear programming generate a solution sequence in which every limit point satisfies the strict complementarity condition. These algorithms include all path-following algorithms and some potential reduction algorithms. The result also holds for the monotone complementarity problem if a strict complementarity solution exists. In general, the limit point is a solution that maximizes the number of its nonzero components among all solutions.
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    Mathematical programming 52 (1991), S. 481-509 
    ISSN: 1436-4646
    Keywords: Interior point methods ; linear programming ; potential function ; search direction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A basic characteristic of an interior point algorithm for linear programming is the search direction. Many papers on interior point algorithms only give an implicit description of the search direction. In this report we derive explicit expressions for the search directions used in many well-known algorithms. Comparing these explicit expressions gives a good insight into the similarities and differences between the various algorithms. Moreover, we give a survey of projected gradient and Newton directions for all potential and barrier functions. This is done both for the affine and projective variants.
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  • 11
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    Mathematical programming 62 (1993), S. 1-14 
    ISSN: 1436-4646
    Keywords: Greedy algorithm ; Monge arrays ; series parallel graphs ; linear programming ; network flow ; transportation problem ; integrality ; convexity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We study the concept of series and parallel composition of linear programming problems and show that greedy properties are inherited by such compositions. Our results are inspired by earlier work on compositions of flow problems. We make use of certain Monge properties as well as convexity properties which support the greedy method in other contexts.
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  • 12
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    Mathematical programming 62 (1993), S. 153-197 
    ISSN: 1436-4646
    Keywords: Complexity analysis ; interior point methods ; Karmarkar's algorithm ; linear programming ; semi-infinite programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Karmarkar's algorithm for linear programming was published in 1984, and it is highly important to both theory and practice. On the practical side some of its variants have been found to be far more efficient than the simplex method on a wide range of very large calculations, while its polynomial time properties are fundamental to research on complexity. These properties depend on the fact that each iteration reduces a “potential function” by an amount that is bounded away from zero, the bound being independent of all the coefficients that occur. It follows that, under mild conditions on the initial vector of variables, the number of iterations that are needed to achieve a prescribed accuracy in the final value of the linear objective function is at most a multiple ofn, wheren is the number of inequality constraints. By considering a simple example that allowsn to be arbitrarily large, we deduce analytically that the magnitude of this complexity bound is correct. Specifically, we prove that the solution of the example by Karmarkar's original algorithm can require aboutn/20 iterations. Further, we find that the algorithm makes changes to the variables that are closely related to the steps of the simplex method.
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  • 13
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    Mathematical programming 57 (1992), S. 325-335 
    ISSN: 1436-4646
    Keywords: Strict complementarity ; interior point algorithms ; linear programming ; optimal face
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract It has been shown [8] that numerous interior-point algorithms for linear programming (LP) generate solution sequences that converge to strict complementarity solutions, or interior solutions on the optimal face. In this note we further establish a theoretical base for Gay's test (Gay, 1989) to identify the optimal face, and develop a new termination procedure to obtain an exact solution on the optimal face. We also report some numerical results for solving a set of LP test problems, each of which has a highly degenerate and unbounded optimal face.
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  • 14
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    Mathematical programming 57 (1992), S. 375-414 
    ISSN: 1436-4646
    Keywords: Leontief matrices ; linear programming ; integer programming ; network flows ; polyhedral combinatorics ; expert systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Leontief substitution systems have been studied by economists and operations researchers for many years. We show how such linear systems are naturally viewed asLeontief substitution flow problems on directed hypergraphs, and that important solution properties follow from structural characteristics of the hypergraphs. We give a strongly polynomial, non-simplex algorithm for Leontief substitution flow problems that satisfy againfree property leading to acyclic extreme solutions. Integrality conditions follow easily from this algorithm. Another structural property,support disjoint reachability, leads to necessary and sufficient conditions for extreme solutions to be binary. In a survey of applications, we show how the Leontief flow paradigm links polyhedral combinatorics, expert systems, mixed integer model formulation, and some problems in graph optimization.
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  • 15
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    Mathematical programming 35 (1986), S. 173-192 
    ISSN: 1436-4646
    Keywords: Computational complexity ; average running time ; simplex algorithm ; linear programming ; linear complementarity problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We show that a particular pivoting algorithm, which we call the lexicographic Lemke algorithm, takes an expected number of steps that is bounded by a quadratic inn, when applied to a random linear complementarity problem of dimensionn. We present two probabilistic models, both requiring some nondegeneracy and sign-invariance properties. The second distribution is concerned with linear complementarity problems that arise from linear programming. In this case we give bounds that are quadratic in the smaller of the two dimensions of the linear programming problem, and independent of the larger. Similar results have been obtained by Adler and Megiddo.
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  • 16
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    Mathematical programming 40 (1988), S. 165-181 
    ISSN: 1436-4646
    Keywords: Relaxation ; network ; integer programming ; linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The strategy of subdividing optimization problems into layers by splitting variables into multiple copies has proved useful as a method for inducing exploitable structure in a variety of applications, particularly those involving embedded pure and generalized networks. A framework is proposed in this paper which leads to new relaxation and restriction methods for linear and integer programming based on our extension of this strategy. This framework underscores the use of constructions that lead to stronger relaxations and more flexible strategies than previous applications. Our results establish the equivalence of all layered Lagrangeans formed by parameterizing the equal value requirement of copied variables for different choices of the principal layers. It is further shown that these Lagrangeans dominate traditional Lagrangeans based on incorporating non-principal layers into the objective function. In addition a means for exploiting the layered Lagrangeans is provided by generating subgradients based on a simple averaging calculation. Finally, we show how this new layering strategy can be augmented by an integrated relaxation/restriction procedure, and indicate variations that can be employed to particular advantage in a parallel processing environment. Preliminary computational results on fifteen real world zero-one personnel assignment problems, comparing two layering approaches with five procedures previously found best for those problems, are encouraging. One of the layering strategies tested dominated all non-layering procedures in terms of both quality and solution time.
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    Mathematical programming 41 (1988), S. 317-325 
    ISSN: 1436-4646
    Keywords: Parametric methods ; linear programming ; anti-cycling pivoting rules
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The traditional perturbation (or lexicographic) methods for resolving degeneracy in linear programming impose decision rules that eliminate ties in the simplex ratio rule and, therefore, restrict the choice of exiting basic variables. Bland's combinatorial pivoting rule also restricts the choice of exiting variables. Using ideas from parametric linear programming, we develop anticycling pivoting rules that do not limit the choice of exiting variables beyond the simplex ratio rule. That is, any variable that ties for the ratio rule can leave the basis. A similar approach gives pivoting rules for the dual simplex method that do not restrict the choice of entering variables.
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    Mathematical programming 42 (1988), S. 125-133 
    ISSN: 1436-4646
    Keywords: Cycling ; degeneracy ; graph theory ; linear programming ; sensitivity analysis ; simplex method
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  • 19
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    OR spectrum 14 (1992), S. 43-52 
    ISSN: 1436-6304
    Keywords: Markov decision processes ; linear programming ; Markovsche Entscheidungsprozesse ; lineare Programmierung
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Description / Table of Contents: Zusammenfassung Separabele Markoffsche Entscheidungsprobleme haben die Eigenschaft, daß für gewisse Paare (i, a) von Zuständeni und zugehörigen Aktionena gilt: (i) die unmittelbare Auszahlung ist die Summe zweier Terme, von denen der eine nur vom Zustand und der andere nur von der Aktion abhängt (ria=si+ta), (ii) die Übergangswahrscheinlichkeiten hängen nur von der Aktion ab und nicht vom Zustand, in dem diese Aktion gewählt wurde. Dieses Modell wurde schon gegen Ende der Sechziger Jahre untersucht. Es wurde bewiesen, daß diskontierte Probleme und undiskontierte Probleme mit nur einer rekurrenten Klasse als lineare Programme mit weniger Variablen als im allgemeinen Modell formuliert werden können. Es war bisher unbekannt, ob auch für undiskontierte Modelle mit mehreren rekurrenten Klassen eine Formulierung mit weniger Variablen existiert. Dieses Problem wird in der vorliegenden Arbeit gelöst: eine solche Formulierung ist möglich. Abschließend werden einige Anwendungen von separablen Modellen angegeben.
    Notes: Summary Separable Markovian decision problems have the property that for certain pairs (i, a) of a statei and an actiona: (i) the immediate reward is the sum of terms due to the current state and action (ria=Si+ta), (ii) the transition probability depends only on the action and not on the state from which the transition occurs. The separable model was studied already in the late sixties. For the discounted case and the unichain undiscounted case a reduced LP formulation was given, which involves a substantially smaller number of variables than in the LP formulation of a general Markov decision problem. It was unknown whether such an efficient formulation was also possible in the multichain case. This paper solves this problem: such an efficient formulation can be obtained. Some applications of separable models are also presented.
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  • 20
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    OR spectrum 18 (1996), S. 131-144 
    ISSN: 1436-6304
    Keywords: Cutting stock ; integer solutions ; heuristics ; linear programming ; column generation ; numerical experiments ; Zuschneideprobleme ; Ganzzahligkeit ; Heuristiken ; Lineare Optimierung ; Spaltengenerierung ; Numerische Experimente
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Description / Table of Contents: Zusammenfassung In der vorliegenden Arbeit betrachten wir das Problem der Bestimmung ganzzahliger Lösungen für das Standardproblem der eindimensionalen Zuschnittplanung. Insbesondere werden eine spezielle Klasse heuristischer Lösungsverfahren, die in der Literatur beschrieben sind, sowie einige naheliegende Varianten dieser Verfahren vorgestellt. Auf der Grundlage eines numerischen Experiments, bei dem 4.000 Probleme zufällig erzeugt und gelöst wurden, werden die Verfahren miteinander verglichen und im Hinblick auf die Kriterien „Lösungsqualität“ und „Rechenzeitbedarf“ beurteilt. Dabei zeigt sich nicht nur, daß zwei Verfahren deutlich besser als die übrigen einzustufen sind, sondern auch, daß mit ihrer Hilfe nahezu jede Problemausprägung des klassischen eindimensionalen Zuschneideproblems optimal gelöst werden kann.
    Notes: Abstract In this paper the problem of generating integer solutions to the standard one-dimensional cutting stock problem is treated. In particular, we study a specific class of heuristic approaches that have been proposed in the literature, and some straightforward variants. These methods are compared with respect to solution quality and computing time. Our evaluation is based on having solved 4,000 randomly generated test problems. Not only will it be shown that two methods are clearly superior to the others but also that they solve almost any instance of the standard one-dimensional cutting stock problem to an optimum.
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    OR spectrum 19 (1997), S. 67-74 
    ISSN: 1436-6304
    Keywords: Deckung des Bedarfs an Klassenräumen ; lineare Optimierung ; gemischt ganzzahlige Optimierung ; Covering classroom requirements ; linear programming ; mixed integer programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Description / Table of Contents: Abstract The city of Hamburg expects for the next years enormously increasing rates of pupils. Covering the additional need of classrooms by new building is very expensive. It would be better to avoid this by making good use of the existing resources. The possible steps for covering the requirements and the restrictions are explained subsequently. There are a lot of alternatives, which must be coordinated for a “good” solution in a suitable way. The mathematical model for this problem is described furthermore. The model in question belongs to the class of mixed integer problems. Finally the standard optimization software SCICONIC, which is used for solving the mixed integer problem, is introduced and the embedding of SCICONIC into the architecture of the evolved planning system is described. Because the users of the system are officers without knowledge in electronic data processing and mathematical programming, a user friendly interface is of special importance in this case. This paper does not include new mathematical cognition, but describes the skilled use of known OR-techniques in a real software project.
    Notes: Zusammenfassung Die Stadt Hamburg sieht in den nächsten Jahren eine beträchtliche Schülerwelle auf sich zukommen. Daraus resultiert zusätzlicher Klassenraumbedarf, der durch Neubau nur sehr kostspielig gedeckt werden kann. Durch systematische Ausnutzung von bestehenden Raumreserven lassen sich teure Neubaumaßnahmen weitgehend vermeiden. Im folgenden werden zunächst die möglichen Maßnahmen zur Raumbedarfsdeckung mit ihren einschränkenden Bedingungen aufgezeigt. Die Maßnahmen erlauben eine Vielzahl von Handlungsalternativen, die zum wirksamen Einsatz optimal aufeinander abgestimmt werden müssen. Das mathematische Modell zur Lösung dieses Problems wird in der weiteren Folge beschrieben. Es handelt sich um ein gemischt ganzzahliges Optimierungsproblem. Abschließend wird die zur Lösung eingesetzte Standard-Optimierungs-Software SCICONIC vorgestellt und die Einbindung von SCICONIC in das entwickelte Raumplanungssystem geschildert. Da die Anwender des Systems Sachbearbeiter ohne DV- und OR-Erfahrung sind, hat hier Benutzerfreundlichkeit eine besonders hohe Bedeutung. Der vorliegende Aufsatz erhebt nicht den Anspruch auf neue mathematische Erkenntnisse, sondern er beschreibt die fachgerechte Anwendung bekannter OR-Verfahren in einem Software-Projekt der Praxis.
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    OR spectrum 19 (1997), S. 67-74 
    ISSN: 1436-6304
    Keywords: Schlüsselwörter: Deckung des Bedarfs an Klassenräumen ; lineare Optimierung ; gemischt ganzzahlige Optimierung ; Key words: Covering classroom requirements ; linear programming ; mixed integer programming
    Source: Springer Online Journal Archives 1860-2000
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    Description / Table of Contents: Abstract. The city of Hamburg expects for the next years enormously increasing rates of pupils. Covering the additional need of classrooms by new building is very expensive. It would be better to avoid this by making good use of the existing resources. The possible steps for covering the requirements and the restrictions are explained subsequently. There are a lot of alternatives, which must be coordinated for a ``good'' solution in a suitable way. The mathematical model for this problem is described furthermore. The model in question belongs to the class of mixed integer problems. Finally the standard optimization software SCICONIC, which is used for solving the mixed integer problem, is introduced and the embedding of SCICONIC into the architecture of the evolved planning system is described. Because the users of the system are officers without knowledge in electronic data processing and mathematical programming, a user friendly interface is of special importance in this case. This paper does not include new mathematical cognition, but describes the skilled use of known OR-techniques in a real software project.
    Notes: Zusammenfassung. Die Stadt Hamburg sieht in den nächsten Jahren eine beträchtliche Schülerwelle auf sich zukommen. Daraus resultiert zusätzlicher Klassenraumbedarf, der durch Neubau nur sehr kostspielig gedeckt werden kann. Durch systematische Ausnutzung von bestehenden Raumreserven lassen sich teure Neubaumaßnahmen weitgehend vermeiden. Im folgenden werden zunächst die möglichen Maßnahmen zur Raumbedarfsdeckung mit ihren einschränkenden Bedingungen aufgezeigt. Die Maßnahmen erlauben eine Vielzahl von Handlungsalternativen, die zum wirksamen Einsatz optimal aufeinander abgestimmt werden müssen. Das mathematische Modell zur Lösung dieses Problems wird in der weiteren Folge beschrieben. Es handelt sich um ein gemischt ganzzahliges Optimierungsproblem. Abschließend wird die zur Lösung eingesetzte Standard-Optimierungs-Software SCICONIC vorgestellt und die Einbindung von SCICONIC in das entwickelte Raumplanungssystem geschildert. Da die Anwender des Systems Sachbearbeiter ohne DV- und OR-Erfahrung sind, hat hier Benutzerfreundlichkeit eine besonders hohe Bedeutung. Der vorliegende Aufsatz erhebt nicht den Anspruch auf neue mathematische Erkenntnisse, sondern er beschreibt die fachgerechte Anwendung bekannter OR-Verfahren in einem Software-Projekt der Praxis.
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    Annals of operations research 5 (1985), S. 599-612 
    ISSN: 1572-9338
    Keywords: Microcomputers ; spreadsheets ; linear programming ; optimization ; software
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract This paper discusses the advantages of using spreadsheets for problem specification and report generation in optimization projects. It summarizes some of the mathematical programming software which is compatible with popular spreadsheets. A small production planning problem is used to illustrate the steps in input and processing of the results. Two programs are compared in detail.
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    Annals of operations research 99 (2000), S. 95-122 
    ISSN: 1572-9338
    Keywords: cutting plane ; path following ; analytic center ; linear programming ; convex programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract A cutting plane method for linear programming is described. This method is an extension of Atkinson and Vaidya's algorithm, and uses the central trajectory. The logarithmic barrier function is used explicitly, motivated partly by the successful implementation of such algorithms. This makes it possible to maintain primal and dual iterates, thus allowing termination at will, instead of having to solve to completion. This algorithm has the same complexity (O(nL 2) iterations) as Atkinson and Vaidya's algorithm, but improves upon it in that it is a “long-step” version, while theirs is a “short-step” one in some sense. For this reason, this algorithm is computationally much more promising as well. This algorithm can be of use in solving combinatorial optimization problems with large numbers of constraints, such as the Traveling Salesman Problem.
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    Annals of operations research 14 (1988), S. 245-289 
    ISSN: 1572-9338
    Keywords: 90C27 ; 68Q15 ; 68Q25 ; 68Rxx ; Parallel computer ; computational complexity ; polylog parallel algorithm ; P-completeness ; sorting ; shortest paths ; minimum spanning tree ; matching ; maximum flow ; linear programming ; knapsack ; scheduling ; traveling salesman ; dynamic programming ; branch and bound
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    Notes: Abstract This is a review of the literature on parallel computers and algorithms that is relevant for combinatorial optimization. We start by describing theoretical as well as realistic machine models for parallel computations. Next, we deal with the complexity theory for parallel computations and illustrate the resulting concepts by presenting a number of polylog parallel algorithms andP-completeness results. Finally, we discuss the use of parallelism in enumerative methods.
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    Annals of operations research 14 (1988), S. 41-59 
    ISSN: 1572-9338
    Keywords: Parallel algorithms ; SOR ; gradient projection ; linear programming ; linear complementarity problem
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    Notes: Abstract A gradient projection successive overrelaxation (GP-SOR) algorithm is proposed for the solution of symmetric linear complementary problems and linear programs. A key distinguishing feature of this algorithm is that when appropriately parallelized, the relaxation factor interval (0, 2) isnot reduced. In a previously proposed parallel SOR scheme, the substantially reduced relaxation interval mandated by the coupling terms of the problem often led to slow convergence. The proposed parallel algorithm solves a general linear program by finding its least 2-norm solution. Efficiency of the algorithm is in the 50 to 100 percent range as demonstrated by computational results on the CRYSTAL token-ring multicomputer and the Sequent Balance 21000 multiprocessor.
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    Annals of operations research 2 (1984), S. 59-94 
    ISSN: 1572-9338
    Keywords: Efficiency ; Pareto optimality ; production functions ; returns to scale ; nondiscretionary inputs ; linear programming ; fractional programming
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    Topics: Mathematics , Economics
    Notes: Abstract This paper serves as an introduction to a series of three papers which are directed to different aspects of DEA (Data Envelopment Analysis) as follows: (1) uses and extensions of window analyses' to study DEA efficiency measures with an illustrative applications to maintenance activities for U.S. Air Force fighter wings, (2) a comparison of DEA and regression approaches to identifying and estimating, sources of inefficiency by means of artificially generated data, and (3) an extension of ordinary (linear programming) sensitivity analyses to deal with special features that require attention in DEA. Background is supplied in this introductory paper with accompanying proofs and explanations to facilitate understanding of what DEA provides in the way of underpinning for the papers that follow. An attempt is made to bring readers abreast of recent progress in DEA research and uses. A synoptic history is presented along with brief references to related work, and problems requiring attention are also indicated and possible research approaches also suggested.
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    Annals of operations research 46-47 (1993), S. 249-269 
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    Keywords: Convex polytopes ; degeneracy ; linear programming ; simplex method
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    Notes: Abstract Finding the incident edges to a degenerate vertex of a polyhedron is a non-trivial problem. So pivoting methods generally involve a perturbation argument to overcome the degeneracy problem. But the perturbation entails a bursting of each degenerate vertex into a cluster of nondegenerate vertices. The aim of this paper is to give some bounds on the number of these perturbed vertices.
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    Annals of operations research 58 (1995), S. 67-98 
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    Keywords: Column generation ; convex programming ; cutting plane methods ; decomposition ; interior point method ; linear programming ; logarithmic barrier function ; nonsmooth optimization ; semi-infinite programming
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    Notes: Abstract The paper presents a logarithmic barrier cutting plane algorithm for convex (possibly non-smooth, semi-infinite) programming. Most cutting plane methods, like that of Kelley, and Cheney and Goldstein, solve a linear approximation (localization) of the problem and then generate an additional cut to remove the linear program's optimal point. Other methods, like the “central cutting” plane methods of Elzinga-Moore and Goffin-Vial, calculate a center of the linear approximation and then adjust the level of the objective, or separate the current center from the feasible set. In contrast to these existing techniques, we develop a method which does not solve the linear relaxations to optimality, but rather stays in the interior of the feasible set. The iterates follow the central path of a linear relaxation, until the current iterate either leaves the feasible set or is too close to the boundary. When this occurs, a new cut is generated and the algorithm iterates. We use the tools developed by den Hertog, Roos and Terlaky to analyze the effect of adding and deleting constraints in long-step logarithmic barrier methods for linear programming. Finally, implementation issues and computational results are presented. The test problems come from the class of numerically difficult convex geometric and semi-infinite programming problems.
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    Annals of operations research 97 (2000), S. 143-162 
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    Keywords: portfolio selection ; multiple criteria ; linear programming ; equity
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    Notes: Abstract The portfolio selection problem is usually considered as a bicriteria optimization problem where a reasonable trade-off between expected rate of return and risk is sought. In the classical Markowitz model the risk is measured with variance, thus generating a quadratic programming model. The Markowitz model is frequently criticized as not consistent with axiomatic models of preferences for choice under risk. Models consistent with the preference axioms are based on the relation of stochastic dominance or on expected utility theory. The former is quite easy to implement for pairwise comparisons of given portfolios whereas it does not offer any computational tool to analyze the portfolio selection problem. The latter, when used for the portfolio selection problem, is restrictive in modeling preferences of investors. In this paper, a multiple criteria linear programming model of the portfolio selection problem is developed. The model is based on the preference axioms for choice under risk. Nevertheless, it allows one to employ the standard multiple criteria procedures to analyze the portfolio selection problem. It is shown that the classical mean-risk approaches resulting in linear programming models correspond to specific solution techniques applied to our multiple criteria model.
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    Order 14 (1997), S. 269-278 
    ISSN: 1572-9273
    Keywords: preemptive scheduling ; non preemptive scheduling ; linear programming
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    Topics: Mathematics
    Notes: Abstract The general non preemptive multiprocessor scheduling problem (NPMS) is NP-Complete, while in many specific cases, the same problem is Time-polynomial. A first connection between PMS and linear programming was established by Yannanakis, Sauer and Stone, who associated to any PMS instance some specific linear program. The main result inside this paper consists in a characterization of the partially ordered structures which allow the optimal values of any associated PMS instance to be equal to the optimal values of the corresponding linear programs.
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    Annals of operations research 45 (1993), S. 349-372 
    ISSN: 1572-9338
    Keywords: Incomplete market ; trading constraints ; linear programming ; optimal portfolio ; duality
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    Notes: Abstract We study a consistent treatment for both the multi-period portfolio selection problem and the option attainability problem by a dual approach. We assume that time is discrete, the horizon is finite, the sample space is finite and the number of securities is less than that of the possible securities price transitions, i.e. an incomplete security market. The investor is prohibited from investing stocks more than given linear investment amount constraints at any time and he maximizes an expected additive utility function for the consumption process. First we give a set of budget feasibility conditions so that a consumption process is attainable by an admissible portfolio process. To establish this relation, we used an algorithmic approach which has a close connection with the linear programming duality. Then we prove the unique existence of a primal optimal solution from the budget feasibility conditions. Finally, we formulate a dual control problem and establish the duality between primal and dual control problems.
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    Annals of operations research 46-47 (1993), S. 497-508 
    ISSN: 1572-9338
    Keywords: Simplex method ; linear programming ; pivoting ; degeneracy ; exterior point algorithms ; cycling
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    Notes: Abstract We present an exterior point simplex type algorithm that possesses a new monotonic property. A dual feasible basic solution is required to start with. Intermediate solutions are neither primal nor dual feasible. Cycling-free pivoting rules and an exponentional example are presented.
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    Annals of operations research 58 (1995), S. 519-531 
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    Keywords: Driver scheduling ; multiple objectives ; linear programming ; dual simplex
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    Notes: Abstract A Mathematical Programming model of a driver scheduling system is described. This consists of set covering and partitioning constraints, possibly user-supplied side constraints, and two pre-emptively ordered objectives. The previous solution strategy addressed the two objectives using separate Primal Simplex optimisations; a new strategy uses a single weighted objective function and a Dual Simplex algorithm initiated by a specially developed heuristic. Computational results are reported.
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    Annals of operations research 43 (1993), S. 427-441 
    ISSN: 1572-9338
    Keywords: Petroleum production ; linear programming
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    Notes: Abstract The purpose of this paper is to describe a planning model for the management of approximately 130 petroleum-producing wells in the North Sea. The objective is to form a better basis for the decisions about which wells to produce from and which to shut down during a period. Every well is dealt with individually as the production potential and chemical composition are different. The total flow consists of six saleable components: gas, four NGL products, and oil. The production may be curtailed due to the capacities of the platforms, gathering centre, pipelines and refinery plants. The total gas production is available for fulfilling the gas contracts, injecting the gas into the reservoirs or using the gas as fuel. There exist contracts for some of the NGL products, while the rest of the NGL products and oil are sold on the free market. The well-management model is solved by means of a standard mathematical programming code, and computational results are given for a planning problem with four different data sets.
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    ISSN: 1572-9338
    Keywords: Degeneracy/cycling ; non-Archimedean data envelopment analysis ; linear programming
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    Notes: Abstract A non-Archimedean effective anti-degeneracy/cycling method for linear programming models, especially Data Envelopment Analysis (DEA), processing networks and advertising media mix models is herein developed. It has given a tenfold speed increase plus elimination of cycling difficulties over conventional Marsden or Kennington/Ali LP software modules in a 1000 LP DEA application.
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    Annals of operations research 46-47 (1993), S. 393-408 
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    Keywords: Degeneracy ; linear programming ; degeneracy graphs
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    Notes: Abstract Degenerate optima in linear programming problems lead in a canonical way to so-called o-degeneracy graphs as subgraphs of degeneracy graphs induced by the set of optimal bases. Fundamental questions about the structure of o-degeneracy graphs suggest the closer inspection of some properties of these graphs, such as, for example, the connectivity and the complexity. Finally, some open questions are pointed out.
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    Annals of operations research 57 (1995), S. 233-249 
    ISSN: 1572-9338
    Keywords: Steiner tree ; game theory ; cost allocation ; integer programming ; linear programming
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    Notes: Abstract A cost allocation problem arising from the Steiner Tree (ST) problem in networks is analyzed. This cost allocation problem is formulated as a cost cooperative game in characteristic function form, referred to as theST-game. The class ofST games generalizes the class of minimum cost spanning tree games which were used in the literature to analyze a variety of cost allocation problems. In general, the core of anST-game may be empty. We construct an efficient Core Heuristic to compute a “good” lower bound on the maximum fraction of the total cost that can be distributed among users while satisfying the core constraints. Based on the Core Heuristic, we also provide a sufficient condition for a givenST not to be optimal for the linear programming relaxation of an integer programming formulation of theST problem. The Core Heuristic was implemented and tested on 76 data sets from the literature (Wong's, Aneja's and Beasley's Steiner tree problems). Core points were found for 69 of these cases, and points “close” to the core were computed in the others.
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    Annals of operations research 51 (1994), S. 45-59 
    ISSN: 1572-9338
    Keywords: Multiple objective ; linear programming ; stochastic ; decision support system
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    Notes: Abstract Most of the multiple objective linear programming (MOLP) methods which have been proposed in the last fifteen years suppose deterministic contexts, but because many real problems imply uncertainty, some methods have been recently developed to deal with MOLP problems in stochastic contexts. In order to help the decision maker (DM) who is placed before such stochastic MOLP problems, we have built a Decision Support System called PROMISE. On the one hand, our DSS enables the DM to identify many current stochastic contexts: risky situations and also situations of partial uncertainty. On the other hand, according to the nature of the uncertainty, our DSS enables the DM to choose the most appropriate interactive stochastic MOLP method among the available methods, if such a method exists, and to solve his problem via the chosen method.
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    Annals of operations research 62 (1996), S. 419-437 
    ISSN: 1572-9338
    Keywords: Big-M Phase I procedure ; convex quadratic programming ; interior point methods ; linear programming ; method of centers ; multidirectional search direction ; nonconvex quadratic programming ; recentering
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    Notes: Abstract In this paper, we present an interior point algorithm for solving both convex and nonconvex quadratic programs. The method, which is an extension of our interior point work on linear programming problems efficiently solves a wide class of largescale problems and forms the basis for a sequential quadratic programming (SQP) solver for general large scale nonlinear programs. The key to the algorithm is a three-dimensional cost improvement subproblem, which is solved at every interation. We have developed an approximate recentering procedure and a novel, adaptive big-M Phase I procedure that are essential to the sucess of the algorithm. We describe the basic method along with the recentering and big-M Phase I procedures. Details of the implementation and computational results are also presented.
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    Annals of operations research 64 (1996), S. 197-210 
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    Keywords: Feasibility ; uncapacitated network ; Gale-Hoffman inequality ; linear programming ; frame ; cut ; facet ; polar matrix
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    Topics: Mathematics , Economics
    Notes: Abstract The purpose of this paper is to investigate the effect of individual arcs and nodes on the description of feasibility in an uncapacitated network. This is done by developing an iterative algorithm for finding all (necessary) Gale-Hoffman inequalities for the network.
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    Annals of operations research 73 (1997), S. 253-276 
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    Keywords: Data Envelopment Analysis ; game theory ; efficiency ; linear programming
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    Notes: Abstract Dominant Competitive Factors, unique solutions to a new class of two-person ratio efficiency games, are introduced as a means for distinguishing exceptional aspects of individual performance. The vectors of input-output multipliers thus obtained may be analyzed collectively so that commonalities within groups and differences across groups may be discovered. The method is applied to "Program Follow-Through", the original impetus for developing Data Envelopment Analysis. Our results are compared with those of the earlier study, whereupon substantial new insights are obtained.
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    Annals of operations research 73 (1997), S. 299-321 
    ISSN: 1572-9338
    Keywords: Discriminant Analysis ; linear programming ; Data Envelopment Analysis ; insurance
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    Notes: Abstract Data Envelopment Analysis (DEA) and Discriminant Analysis (DA) are similar in that both may be used to classify units as exhibiting either good or poor performance. Both use linear programming to select a set of factor weights that determines group membership relative to a "threshold" or hyperplane. This similarity was pointed out in an earlier paper, in which several methods which combine aspects of DA and DEA were suggested. This paper further develops one of these hybrid methods, which can be described as an efficiency approach to Discriminant Analysis. The various formulation options are considered with respect to their effects on solution quality and stability. The stability issue is raised by the fact that solution equivalence under data transformation (including both translation and rotation) is considered important in DA, and has significantly affected model formulation. Thus, the data transformation issue is studied for the hybrid method, and also for DEA. The hybrid method is applied to an insurance data set, where some firms are solvent and others in financial distress, to further evaluate the method and its possible formulations. DA methods are applied to the same data set to provide a basis for comparison. The hybrid method is shown to outperform the general discriminant models.
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    Annals of operations research 97 (2000), S. 111-129 
    ISSN: 1572-9338
    Keywords: repo contracts ; portfolio management ; linear programming ; 90A09 ; 90C05 ; 68U20
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    Notes: Abstract In this paper we present a model for management of bond portfolio including financing and investment repo contracts. Different specifications are suggested in order to reduce the problem to a linear programming problem and to consider a self-financing portfolio. The models are tested on historical data assuming a technical time scale equal to the minimum length of the contracts in the portfolio. We also compared different operative strategies on a time horizon of one month.
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    ISSN: 1572-9397
    Keywords: set partitioning ; preprocessing ; linear programming ; Lagrangian dual
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    Notes: Abstract Given a finite ground set, a set of subsets, and costs on the subsets, the set partitioning problem is to find a minimum cost partition of the ground set. Many combinatorial optimization problems can be formulated as set partitioning problems. We present an approximation algorithm that produces high-quality solutions in an acceptable amount of computation time. The algorithm is iterative and combines problem size-reduction techniques, such as logical implications derived from feasibility and optimality conditions and reduced cost fixing, with a primal heuristic based on cost perturbations embedded in a Lagrangian dual framework, and cutting planes. Computational experiments illustrate the effectiveness of the approximation algorithm.
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    BIT 10 (1970), S. 95-105 
    ISSN: 1572-9125
    Keywords: Land-use planning ; accessibility ; uncertainty ; game theory ; linear programming
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    Topics: Mathematics
    Notes: Abstract One objective in regional planning is the creation of communities with great accessibility. Thus we should plan the locations of inhabitants and the activities of the region so that the total accessibility will be maximized subject to some restrictions. This is a quadratic programming problem, which can be solved by quadratic programming techniques, but we cannot then take into account the uncertainties of the problem. In this paper a new criterion function is proposed for accessibility, uncertainty problems in regional land-use planning. It is derived from Hurwicz's generalized maximin principle. Many advantages are gained, for the planning problem is separated into linear programming problems, the uncertainties are taken into consideration as in game theory and the methods of parametric programming are available. A simplified problem of the populations of three town areas is studied and the method is generalized for problems of many activities and areas.
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    BIT 31 (1991), S. 2-14 
    ISSN: 1572-9125
    Keywords: E.1 ; F.2.2 ; computational geometry ; intersection detection ; geometric duality ; multidimensional search ; hyperplane-polyhedron intersection ; polyhedron-polyhedron intersection ; arbitrary dimensions ; linear programming
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    Notes: Abstract This paper presents a dual approach to detect intersections of hyperplanes and convex polyhedra in arbitrary dimensions. Ind dimensions, the time complexities of the dual algorithms areO(2 d logn) for the hyperplane-polyhedron intersection problem, andO((2d) d−1 log d−1 n) for the polyhedron- polyhedron intersection problem. These results are the first of their kind ford 〉 3. In two dimensions, these time bounds are achieved with linear space and preprocessing. In three dimensions, the hyperplane-polyhedron intersection problem is also solved with linear space and preprocessing; quadratic space and preprocessing, however, is required for the polyhedron-polyhedron intersection problem. For generald, the dual algorithms require $$O(n^{2^d } )$$ space and preprocessing. All of these results readily extend to unbounded polyhedra.
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    BIT 23 (1983), S. 403-405 
    ISSN: 1572-9125
    Keywords: Vertex ranking ; fractional programming ; linear programming
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    Topics: Mathematics
    Notes: Abstract In this paper the problem of ranking vertices in the linear fractional programming problem is considered. It is shown that a class of vertex ranking algorithms for the linear programming problem can be used with only minor modifications.
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    OR spectrum 19 (1997), S. 147-157 
    ISSN: 1436-6304
    Keywords: production ; scheduling ; printed circuit board assembly ; modelling ; linear programming ; aggregational error ; decision support ; Produktion ; Ablaufplanung ; Leiterplattenbestückung ; Modellierung ; lineare Programmierung ; Aggregationsfehler ; Entscheidungsunterstützung
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    Description / Table of Contents: Zusammenfassung Bei der Kleinserienmontage von Leiterplatten besteht das Problem der Einlastungsplanung darin, ein Tagesprogramm an Produktionsaufträgen zusammenzustellen, die gemeinsam in das Produktionssystem eingeschleust werden. Jeder Produktionsauftrag entspricht einem bestimmten Leiterplattentyp. Wechselt man bei der automatischen Bestückung von Leiterplatten zu einem neuen Leiterplattentyp, so fallen erhebliche Rüstzeiten an, die davon abhängen, wie viele Bauteilezuführungen im Magazin der Bestückungsautomaten ausgewechselt werden müssen. Zur Unterstützung dieses Entscheidungsproblems werden zwei unterschiedliche Modelle der linearen Optimierung entwickelt. Die beiden Modelle unterscheiden sich vor allem durch ihren Aggregationsgrad und ihren Rechenaufwand. Zur Verringerung des Aggregationsfehlers wird ein auf der Fuzzy-Set-Theorie beruhender Ansatz zur Abschätzung der bei automatischen SMD-Bestückungsautomanten auftretenden Rüstzeiten entwickelt. Hierbei wird als industrielles Anwendungsbeispiel die Leiterplattenbestückung in einem bedeutenden Elektronikunternehmen betrachtet. Die durchgeführte numerische Untersuchung zeigt, daß das hochaggregierte Fuzzy-LP-Modell zu hinreichend genauen Lösungen führt und erheblich geringeren Rechenaufwand verursacht als ein detaillierteres LP-Modell. Außerdem wird die praktische Eignung des Fuzzy-LP-Modells für den Einsatz innerhalb eines interaktiven Entscheidungsunterstützungssystems verdeutlicht.
    Notes: Abstract The problem of workload planning in small lot printed circuit board (PCB) assembly concerns the determination of the daily mix of production orders to be released into the production system. When switching from one production order (board type) to another, a considerable set-up time is incurred based on the number of component feeders to be replaced in the component magazine of the assembly machines. To support the order-mix decision faced by a major electronics manufacturer, two versions of a linear programming model are developed. The models differ primarily in their degree of aggregation and their computational effort. In order to reduce the aggregational error incurred, a fuzzy approach is developed to estimate the number of component set-ups at automatic SMD placement machines. Our numerical investigation reveals that sufficiently accurate solutions may be obtained from a highly aggregate fuzzy LP-model and this is achieved with considerably less computational effort than with a more detailed LP-model. We also demonstrate the potential suitability of the fuzzy LP-model for implementation within an interactive decision support system.
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    Journal of mathematical imaging and vision 2 (1992), S. 39-50 
    ISSN: 1573-7683
    Keywords: image processing ; linear programming ; mathematical morphology ; image algebra ; template decomposition
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    Topics: Mathematics
    Notes: Abstract Template decomposition techniques can be useful for improving the efficiency of imageprocessing algorithms. The improved efficiency can be realized either by reorganizing a computation to fit a specialized structure, such as an image-processing pipeline, or by reducing the number of operations used. In this paper two techniques are described for decomposing templates into sequences of 3×3 templates with respect to gray-scale morphological operations. Both techniques use linear programming and are guaranteed to find a decomposition of one exists.
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    Periodica mathematica Hungarica 11 (1980), S. 117-130 
    ISSN: 1588-2829
    Keywords: Primary 26A78 ; Secondary 90C50 ; Positive polynomial ; location of zeros of polynomials ; linear programming ; finite cone
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    Topics: Mathematics
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    Journal of optimization theory and applications 106 (2000), S. 107-127 
    ISSN: 1573-2878
    Keywords: Stochastic control ; linear programming ; numerical comparisons ; numerical verification ; moments ; bounded follower
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    Topics: Mathematics
    Notes: Abstract We provide two approaches to the numerical analysis of stochastic control problems. The analyses rely on linear programming formulations of the control problem and allow numerical comparison between controls and numerical verification of optimality. The formulations characterize the processes through the moments of the induced occupation measures. We deal directly with the processes rather than with some approximation to the processes. Excellent software is readily available, since the computations involve finite-dimensional linear programs.
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    Journal of optimization theory and applications 13 (1974), S. 484-489 
    ISSN: 1573-2878
    Keywords: Convex programming ; duality ; linear programming ; duality gaps
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    Topics: Mathematics
    Notes: Abstract For certain types of mathematical programming problems, a related dual problem can be constructed in which the objective value of the dual problem is equal to the objective function of the given problem. If these two problems do not have equal values, a duality gap is said to exist. No such gap exists for pairs of ordinary dual linear programming problems, but this is not the case for linear programming problems in which the nonnegativity conditionx ⩾ 0 is replaced by the condition thatx lies in a certain convex setK. Duffin (Ref. 1) has shown that, whenK is a cone and a certain interiority condition is fulfilled, there will be no duality gap. In this note, we show that no duality gap exists when the interiority condition is satisfied andK is an arbitrary closed convex set inR n .
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    Journal of optimization theory and applications 55 (1987), S. 1-21 
    ISSN: 1573-2878
    Keywords: Constrained nonsmooth convex programming ; penalty methods ; proximal methods ; linear programming
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    Topics: Mathematics
    Notes: Abstract An implementable algorithm for constrained nonsmooth convex programs is given. This algorithm combines exterior penalty methods with the proximal method. In the case of a linear program, the convergence is finite.
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    Journal of optimization theory and applications 47 (1985), S. 349-368 
    ISSN: 1573-2878
    Keywords: Respiratory physiology ; underdetermined linear systems ; least squares ; linear programming
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    Topics: Mathematics
    Notes: Abstract In recent literature, grossly underdetermined linear models have been developed for certain experimental techniques in respiratory physiology. Because physiologically sensible solutions of the models are reasonably assumed to be smooth, it is possible to recover such solutions using smoothed least-squares techniques. Bounds on the variability of smooth solutions satisfying the model can be generated using linear programming.
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    Journal of optimization theory and applications 61 (1989), S. 487-491 
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    Keywords: Multicriteria optimization ; linear programming ; insoluble problems
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    Notes: Abstract This note concerns a method for analyzing insoluble multicriteria linear programming problems.
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    Journal of optimization theory and applications 62 (1989), S. 225-237 
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    Keywords: Redundancy ; degeneracy ; linear constraints ; linear programming
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    Notes: Abstract This paper presents a degenerate extreme point strategy for active set algorithms which classify linear constraints as either redundant or necessary. The strategy makes use of an efficient method for classifying constraints active at degenerate extreme points. Numerical results indicate that significant savings in the computational effort required to classify the constraints can be achieved.
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    Journal of optimization theory and applications 63 (1989), S. 39-49 
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    Keywords: Inverse gravimetry problem ; linear programming
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    Notes: Abstract From the knowledge of the anomalies of the gravitational field, we reconstruct the mass density distribution in a large region of the state of Bahia (Brazil). This inverse gravimetry problem has been translated into a linear programming problem and solved using the simplex method. Both two-dimensional and three-dimensional models have been considered.
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    Journal of optimization theory and applications 71 (1991), S. 465-484 
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    Keywords: Stabilization ; uncertain systems ; constrained control ; positive invariance ; linear programming
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    Notes: Abstract The linear state feedback synthesis problem for uncertain linear systems with state and control constraints is considered. We assume that the uncertainties are present in both the state and input matrices and they are bounded. The main goal is to find a linear control law assuring that both state and input constraints are fulfilled at each time. The problem is solved by confining the state within a compact and convex positively invariant set contained in the allowable state region. It is shown that, if the controls, the state, and the uncertainties are subject to linear inequality constraints and if a candidate compact and convex polyhedral set is assigned, a feedback matrix assuring that this region is positively invariant for the closed-loop system is found as a solution of a set of linear inequalities for both continuous and discrete time design problems. These results are extended to the case in which additive disturbances are present. The relationship between positive invariance and system stability is investigated and conditions for the existence of positively invariant regions of the polyhedral type are given.
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    Journal of optimization theory and applications 75 (1992), S. 587-602 
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    Keywords: Probabilistic constrained problems ; chance-constrained problems ; linear programming ; duality ; optimization ; convex analysis
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    Notes: Abstract We present two pairs of dually related probabilistic constrained problems as extensions of the linear programming duality concept. In the first pair, a bilinear function appears in the objectives and each objective directly depends on the feasibility set of the other problem, as in the game theoretical formulation of dual linear programs. In the second pair, we reformulate the objectives and eliminate their direct dependence on the feasibility set of the other problem. We develop conditions under which the dually related problems have no duality gap and conditions under which the two pairs of problems are equivalent as far as their optimality sets are concerned.
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    Journal of optimization theory and applications 75 (1992), S. 445-470 
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    Keywords: Augmented Lagrangian algorithms ; linear programming ; global convergence rates ; convex programming
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    Notes: Abstract We introduce new augmented Lagrangian algorithms for linear programming which provide faster global convergence rates than the augmented algorithm of Polyak and Treti'akov. Our algorithm shares the same properties as the Polyak-Treti'akov algorithm in that it terminates in finitely many iterations and obtains both primal and dual optimal solutions. We present an implementable version of the algorithm which requires only approximate minimization at each iteration. We provide a global convergence rate for this version of the algorithm and show that the primal and dual points generated by the algorithm converge to the primal and dual optimal set, respectively.
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    Journal of optimization theory and applications 65 (1990), S. 161-169 
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    Keywords: Linear inequalities ; polyhedra ; linear programming ; Fourier elimination
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    Notes: Abstract The parametric solution of a linear system of inequalitiesAx≤Bb, with parameterb, is considered. Fourier elimination is used to give a facial representation for the set ofb-values for which the system is consistent. Some interesting applications of the problem are discussed. Although the worst case complexity of the method is an exponential function of the size ofA, the computations are intuitive and very simple.
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    Journal of optimization theory and applications 85 (1995), S. 593-612 
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    Keywords: Proximal point methods ; convex programming ; quadratic programming ; linear programming
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    Notes: Abstract The proximal point method for convex optimization has been extended recently through the use of generalized distances (e.g., Bregman distances) instead of the Euclidean one. One advantage of these extensions is the possibility of eliminating certain constraints (mainly positivity) from the subproblems, transforming an inequality constrained problem into a sequence of unconstrained or equality constrained problems. We consider here methods obtained using a certain class of Bregman functions applied to convex quadratic (including linear) programming, which are of the interior-point type. We prove that the limit of the sequence generated by the method lies in the relative interior of the solution set, and furthermore is the closest optimal solution to the initial point, in the sense of the Bregman distance. These results do not hold for the standard proximal point method, i.e., when the square of the Euclidean norm is used as the Bregman distance.
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    Journal of optimization theory and applications 98 (1998), S. 83-108 
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    Keywords: Concave minimization ; reverse convex programs ; non-convex optimization ; global optimization ; test problems ; linear programming ; nonlinear programming ; computational experiments
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    Notes: Abstract This paper presents a method for constructing test problems with known global solutions for concave minimization under linear constraints with an additional convex constraint and for reverse convex programs with an additional convex constraint. The importance of such a construction can be realized from the fact that the well known d.c. programming problem can be formulated in this form. Then, the method is further extended to generate test problems with more than one convex constraint, tight or untight at the global solution.
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    Journal of combinatorial optimization 1 (1998), S. 413-426 
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    Keywords: scheduling ; preemptive scheduling ; release dates ; identical parallel machines ; average completion time ; approximation algorithms ; relaxations ; linear programming
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    Notes: Abstract We consider the problem of scheduling n jobs withrelease dates on m identical parallel machines to minimize the average completion time of the jobs. We prove that the ratio of the average completion time of the optimal nonpreemptive schedule to that of the optimal preemptive schedule is at most 7/3, improving a bound of $$(3 - \frac{1}{m})$$ Shmoys and Wein.
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    Journal of optimization theory and applications 103 (1999), S. 521-542 
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    Keywords: Linear systems ; linear programming ; convexity ; polyhedra ; invariance
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    Notes: Abstract The problem of confining the trajectory of a linear discrete-time system in a given polyhedral domain is addressed through the concept of (A, B)-invariance. First, an explicit characterization of (A, B)-invariance of convex polyhedra is proposed. Such characterization amounts to necessary and sufficient conditions in the form of linear matrix relations and presents two major advantages compared to the ones found in the literature: it applies to any convex polyhedron and does not require the computation of vertices. Such advantages are felt particularly in the computation of the supremal (A, B)-invariant set included in a given polyhedron, for which a numerical method is proposed. The problem of computing a control law which forces the system trajectories to evolve inside an (A, B)-invariant polyhedron is treated as well. Finally, the (A, B)-invariance relations are generalized to persistently disturbed systems.
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    Journal of optimization theory and applications 17 (1975), S. 181-183 
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    Keywords: Mathematical programming ; absolute-value problems ; optimality criteria ; linear programming ; nonconvex programming ; operations research
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    Notes: Abstract This paper considers some programming problems with absolute-value (objective) functions subject to linear constraints. Necessary and sufficient conditions for the existence of finite optimum solutions to these problems are proved.
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    Journal of optimization theory and applications 17 (1975), S. 343-346 
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    Keywords: Convex programming ; duality ; linear programming ; duality gap ; Lagrange multiplier theorem
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    Notes: Abstract In Ref. 1, Soyster has given a rather complicated proof of the absence of a duality gap, under a certain interiority condition, for a variant of a pair of optimization problems introduced by Ben-Israel, Charnes, and Kortanek (Ref. 2). A proof can be given directly (and under weaker conditions) by a simple application of a Lagrange multiplier theorem on convex programming in abstract spaces (Ref. 3).
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    Journal of optimization theory and applications 20 (1976), S. 331-346 
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    Keywords: Dynamic systems ; linear programming ; limiting performance ; indirect synthesis method ; transient disturbances ; Fourier series ; control forces
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    Notes: Abstract It is shown that an indirect synthesis method can be used in the efficient optimal design of multidegree-of-freedon, multidesignelement, nonlinear, transient systems. The technique begins with a limiting performance analysis which requires linear programming for a kinematically linear system, following which the system is selected using system identification methods such that the designed system responds as closely as possible to the limiting performance. The efficiency is a result of the method avoiding the repetitive systems analyses accompanying other numerical optimization methods.
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    Journal of optimization theory and applications 37 (1982), S. 343-353 
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    Keywords: Engineering plasticity ; elastoplastic analysis ; linear programming ; quadratic programming ; alternative optimal solutions
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    Notes: Abstract Two important problems in the area of engineering plasticity are limit load analysis and elastoplastic analysis. It is well known that these two problems can be formulated as linear and quadratic programming problems, respectively (Refs. 1–2). In applications, the number of variables in each of these mathematical programming problems tends to be large. Consequently, it is important to have efficient numerical methods for their solution. The purpose of this paper is to present a method which allows the quadratic programming formulation of the elastoplastic analysis to be reformulated as an equivalent quadratic programming problem which has significantly fewer variables than the original formulation. Indeed, in Section 4, we will present details of an example for which the original quadratic programming formulation required 297 variables and for which the equivalent formulation presented here required only two variables. The method is based on a characterization of the entire family of optimal solutions for a linear programming problem.
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    Journal of optimization theory and applications 29 (1979), S. 53-65 
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    Keywords: Quadratic programming ; linear programming ; engineering plasticity ; limit-load analysis ; displacement analysis
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    Notes: Abstract In engineering plasticity, the behavior of a structure (e.g., a frame or truss) under a variety of loading conditions is studied. Two primary types of analysis are generally conducted. Limit analysis determines the rigid plastic collapse load for a structure and can be formulated as a linear program (LP). Deformation analysis at plastic collapse can be formulated as a quadratic program (QP). The constraints of the two optimization problems are closely related. This paper presents a specialization of the projection method for linear programming for the limit-load analysis problem. The algorithm takes advantage of the relationship between the LP constraints and QP constraints to provide advantageous starting data for the projection method applied to the QP problem. An important feature of the method is that it avoids problems of apparent infeasibility due to roundoff errors. Experimental results are given for two medium-sized problems.
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    Journal of optimization theory and applications 29 (1979), S. 559-571 
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    Keywords: Isotone optimization ; approximation theory ; partially ordered sets ; starshaped functions ; linear programming
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    Notes: Abstract This article considers a curve-fitting problem involving the minimization of the distance from a functionf to a convex cone of functions. A weighted uniform norm is considered as a measure of the distance. The domain of the functions is a partially ordered set, and the convex cone is defined by the isotonicity and nonnegativity conditions on functions. The problem has a linear programming formulation; however, explicit expressions for the optimal solutions have been obtained directly, thereby eliminating the necessity of using linear programming techniques. The results are applied to approximation by starshaped functions.
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    Journal of optimization theory and applications 30 (1980), S. 523-534 
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    Keywords: Vector maximization ; efficient vector ; optimal inverse apir ; linear programming
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    Notes: Abstract This paper presents a new concept of efficient solution for the linear vector maximization problem. Briefly, these solutions are efficient with respect to the constraints, in addition to being efficient with respect to the multiple objectives. The duality theory of linear vector maximization is developed in terms of this solution concept and then is used to formulate the problem as a linear program.
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    Journal of optimization theory and applications 30 (1980), S. 643-661 
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    Keywords: Optimal control ; measures ; Hilbert spaces ; linear programming ; approximation techniques
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    Notes: Abstract Optimal control problems in Hilbert spaces are considered in a measure-theoretical framework. Instead of minimizing a functional defined on a class of admissible trajectory-control pairs, we minimize one defined on a set of measures; this set is defined by the boundary conditions and the differential equation of the problem. The new problem is an infinite-dimensionallinear programming problem; it is shown that it is possible to approximate its solution by that of a finite-dimensional linear program of sufficiently high dimensions, while this solution itself can be approximated by a trajectory-control pair. This pair may not be strictly admissible; if the dimensionality of the finite-dimensional linear program and the accuracy of the computations are high enough, the conditions of admissibility can be said to be satisfied up to any given accuracy. The value given by this pair to the functional measuring the performance criterion can be about equal to theglobal infimum associated with the classical problem, or it may be less than this number. It appears that this method may become a useful technique for the computation of optimal controls, provided the approximations involved are acceptable.
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    Journal of optimization theory and applications 32 (1980), S. 595-597 
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    Keywords: Khatchian's algorithm ; duality theorem ; linear programming
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    Notes: Abstract A short proof of some properties of Khatchian's algorithm is presented using the duality theorem of linear programming.
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    Journal of optimization theory and applications 54 (1987), S. 437-446 
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    Keywords: Parallel algorithms ; linear programming ; complementarity problem ; successive overrelaxation
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    Notes: Abstract A parallel successive overrelaxation (SOR) method is proposed for the solution of the fundamental symmetric linear complementarity problem. Convergence is established under a relaxation factor which approaches the classical value of 2 for a loosely coupled problem. The parallel SOR approach is then applied to solve the symmetric linear complementarity problem associated with the least norm solution of a linear program.
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    Journal of optimization theory and applications 73 (1992), S. 197-203 
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    Keywords: Linear inequalities ; linear complementarity problems ; linear programming ; sensitivity analysis ; tolerance approach
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    Notes: Abstract In this paper, we apply the tolerance approach proposed by Wendell for sensitivity analysis in linear programs to study sensitivity analysis in linear complementarity problems. In the tolerance approach, we find the range or the maximum tolerance within which the coefficients of the right-hand side of the problem can vary simultaneously and independently such that the solution of the original and the perturbed problems have the same index set of nonzero elements.
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    Journal of optimization theory and applications 78 (1993), S. 127-142 
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    Keywords: Karmarkar's algorithm ; lower bounds ; linear programming ; interior-point methods
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    Notes: Abstract We give two results related to Gonzaga's recent paper showing that lower bounds derived from the Todd-Burrell update can be obtained by solving a one-variable linear programming problem involving the centering direction and the affine direction. We show how these results may be used to update the primal solution when using the dual affine variant of Karmarkar's algorithm. This leads to a dual projective algorithm.
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    Journal of optimization theory and applications 78 (1993), S. 247-266 
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    Keywords: Projection methods ; Fejér contraction ; linear complementarity problems ; linear programming ; network programming
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    Notes: Abstract In this paper, based on the idea of a projection and contraction method for a class of linear complementarity problems (Refs. 1 and 2), we develop a class of iterative algorithms for linear programming with linear speed of convergence. The algorithms are used to solve transportation and network problems with up to 10,000 variables. Our experiments indicate that the algorithms are simple, easy to parallelize, and more efficient for some large practical problems.
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    Journal of optimization theory and applications 70 (1991), S. 191-209 
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    Keywords: Diffusion equation ; boundary control ; optimal control ; Radon measures ; linear programming ; approximations
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    Notes: Abstract The existence and numerical estimation of a boundary control for then-dimensional linear diffusion equation is considered. The problem is modified into one consisting of the minimization of a linear functional over a set of Radon measures. The existence of an optimal measure corresponding to the above problem is shown, and the optimal measure is approximated by a finite convex combination of atomic measures. This construction gives rise to a finite-dimensional linear programming problem, whose solution can be used to construct the combination of atomic measures, and thus a piecewise-constant control function which approximates the action of the optimal measure, so that the final state corresponding to the above control function is close to the desired final state, and the value it assigns to the performance criterion is close to the corresponding infimum. A numerical procedure is developed for the estimation of these controls, entailing the solution of large, finite-dimensional linear programming problems. This procedure is illustrated by several examples.
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    Journal of global optimization 9 (1996), S. 153-167 
    ISSN: 1573-2916
    Keywords: Reverse convex programs ; nonconvex optimization ; global optimization ; test problem generation ; linear programming ; nonlinear programming ; computational experiments
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    Notes: Abstract This paper presents computational experience with a rather straight forward implementation of an edge search algorithm for obtaining the globally optimal solution for linear programs with an additional reverse convex constraint. The paper's purpose is to provide a collection of problems, with known optimal solutions, and performance information for an edge search implementation so that researchers may have some benchmarks with which to compare new methods for reverse convex programs or concave minimization problems. There appears to be nothing in the literature that provides computational experience with a basic edge search procedure. The edge search implementation uses a depth first strategy. As such, this paper's implementation of the edge search algorithm is a modification of Hillestad's algorithm [11]. A variety of test problems is generated by using a modification of the method of Sung and Rosen [20], as well as a new method that is presented in this paper. Test problems presented may be obtained at ftp://newton.ee.ucla.edu/nonconvex/pub/.
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    Journal of global optimization 12 (1998), S. 175-195 
    ISSN: 1573-2916
    Keywords: Multiple criteria ; linear programming ; disaggregation method ; ordinalregression analysis
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    Notes: Abstract A new model to assess customer satisfaction is developed through this paper. The proposed model is based on the principles of multicriteria analysis, using ordinal regression techniques. The procedure uses survey's data on customer satisfaction criteria and disaggregates simultaneously all the global satisfaction judgments via a linear programming disaggregation formulation. The model provides collective global and partial satisfaction functions as well as average satisfaction indices. These results sufficiently describe customer behavior and they can be used in the strategic planning of an organization. The implementation of the model in three real world applications is used for illustration and for testing the model's reliability. Finally, several extensions and future research in the area of customer satisfaction analysis are discussed.
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    Journal of optimization theory and applications 106 (2000), S. 511-525 
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    Keywords: linear programming ; game theory ; proper equilibria
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    Notes: Abstract In this paper, we study the optimal solutions of a dual pair of linear programming problems that correspond to the proper equilibria of their associated matrix game. We give conditions ensuring the existence of such solutions, show that they are especially robust under perturbation of right-hand-side terms, and describe a procedure to obtain them.
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    Journal of optimization theory and applications 14 (1974), S. 305-318 
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    Keywords: Integer programming ; mathematical programming ; scheduling ; linear programming
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    Notes: Abstract The problem of scheduling orders at each facility of a large integrated steel mill is considered. Orders are received randomly, and delivery dates are established immediately. Each order is filled by converting raw materials into a finished saleable steel product by a fixed sequence of processes. The application of a deterministic mixed integer linear programming model to the order scheduling problem is given. One important criterion permitted by the model is to process the orders in a sequence which minimizes the total tardiness from promised delivery for all orders; alternative criteria are also possible. Most practical constraints which arise in steelmaking can be considered within the formulation. In particular, sequencing and resource availability constraints are handled easily. The order scheduling model given here contains many variables and constraints, resulting in computational difficulties. A decomposition algorithm is devised for solving the model. The algorithm is a special case of Benders partitioning. Computational experience is reported for a large-scale problem involving scheduling 102 orders through ten facilities over a six-week period. The exact solution to the large-scale problem is compared with schedules determined by several heuristic dispatching rules. The dispatching rules took into consideration such things as due date, processing time, and tardiness penalties. None of the dispatching rules found the optimal solution.
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    Journal of optimization theory and applications 74 (1992), S. 305-316 
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    Keywords: Geometric programming ; linear programming ; complementary slackness ; Lagrange multipliers
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    Notes: Abstract When the terms in a convex primal geometric programming (GP) problem are multiplied by slack variables whose values must be at least unity, the invariance conditions may be solved as constraints in a linear programming (LP) problem in logarithmically transformed variables. The number of transformed slack variables included in the optimal LP basis equals the degree of difficulty of the GP problem, and complementary slackness conditions indicate required changes in associated GP dual variables. A simple, efficient search procedure is used to generate a sequence of improving primal feasible solutions without requiring the use of the GP dual objective function. The solution procedure appears particularly advantageous when solving very large geometric programming problems, because only the right-hand constants in a system of linear equations change at each iteration.
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    Journal of optimization theory and applications 76 (1993), S. 165-182 
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    Keywords: Dynamic programming ; linear programming ; difference equations
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    Notes: Abstract In this paper, the general solutions to the optimal policy and the limit theorem forn-stage,m-reusableness rate production planning are obtained for the problem with free terminal point. In addition, general solutions to the problem with fixed terminal point are obtained. The links between the solutions to these problems are discussed.
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    Journal of optimization theory and applications 65 (1990), S. 29-40 
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    Keywords: Constrained control ; optimal control ; linear programming ; perturbed systems
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    Notes: Abstract The problem of control in the presence of unknown but limited disturbance for a discrete-time linear system with polyhedral input and state bounds is investigated. Two problems are considered: that of reaching an assigned target set in the state space; and that of keeping the state in a given region using the available controls. In both cases, a solution is given via linear programming. A computational procedure for the control synthesis is proposed which can be implemented to obtain a feedback control.
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    Journal of optimization theory and applications 80 (1994), S. 3-18 
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    Keywords: Multiple criteria decision making ; efficient set ; global optimization ; linear programming
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    Notes: Abstract Recently, researchers and practitioners have been increasingly interested in the problem (P) of maximizing a linear function over the efficient set of a multiple objective linear program. Problem (P) is generally a difficult global optimization problem which requires numerically intensive procedures for its solution. In this paper, simple linear programming procedures are described for detecting and solving four special cases of problem (P). When solving instances of problem (P), these procedures can be used as screening devices to detect and solve these four special cases.
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    Journal of optimization theory and applications 81 (1994), S. 343-354 
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    Keywords: Reverse convex programming ; nonconvex programming ; nonlinear programming ; linear programming ; test problems ; global optimization
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    Notes: Abstract A method of constructing test problems with known global solution for a class of reverse convex programs or linear programs with an additional reverse convex constraint is presented. The initial polyhedron is assumed to be a hypercube. The method then systematically generates cuts that slice the cube in such a way that a prespecified global solution on its edge remains intact. The proposed method does not require the solution of linear programs or systems of linear equations as is often required by existing techniques.
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    Journal of optimization theory and applications 100 (1999), S. 327-348 
    ISSN: 1573-2878
    Keywords: Convex programming ; semidefinite programming ; linear matrix inequalities ; linear programming ; constraint-aggregation method ; minimum-penalty path ; exterior path-following methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A semidefinite programming problem is a mathematical program in which the objective function is linear in the unknowns and the constraint set is defined by a linear matrix inequality. This problem is nonlinear, nondifferentiable but convex. It covers several standard problems, such as linear and quadratic programming, and has many applications in engineering. In this paper, we introduce the notion of minimal-penalty path, which is defined as the collection of minimizers for a family of convex optimization problems, and propose two methods for solving the problem by following the minimal-penalty path from the exterior of the feasible set. Our first method, which is also a constraint-aggregation method, achieves the solution by solving a sequence of linear programs, but exhibits a zigzagging behavior around the minimal-penalty path. Our second method eliminates the above drawback by following efficiently the minimum-penalty path through the centering and ascending steps. The global convergence of the methods is proved and their performance is illustrated by means of an example.
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  • 91
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    Journal of optimization theory and applications 46 (1985), S. 55-66 
    ISSN: 1573-2878
    Keywords: Optimal value function ; linear programming ; sensitivity analysis ; parametric programming ; multiple-objective linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Often, the coefficients of a linear programming problem represent estimates of true values of data or are subject to systematic variations. In such cases, it is useful to perturb the original data and to either compute, estimate, or otherwise describe the values of the functionf which gives the optimal value of the linear program for each perturbation. If the right-hand derivative off at a chosen point exists and is calculated, then the values off in a neighborhood of that point can be estimated. However, if the optimal solution set of either the primal problem or the dual problem is unbounded, then this derivative may not exist. In this note, we show that, frequently, even if the primal problem or the dual problem has an unbounded optimal solution set, the nature of the values off at points near a given point can be investigated. To illustrate the potential utility of our results, their application to two types of problems is also explained.
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  • 92
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    Journal of optimization theory and applications 75 (1992), S. 101-132 
    ISSN: 1573-2878
    Keywords: Diffusion equation ; optimal control ; Radon measures ; optimization ; linear programming ; moment problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The present paper is concerned with an optimal control problem for then-dimensional diffusion equation with a sequence of Radon measures as generalized control variables. Suppose that a desired final state is not reachable. We enlarge the set of admissible controls and provide a solution to the corresponding moment problem for the diffusion equation, so that the previously chosen desired final state is actually reachable by the action of a generalized control. Then, we minimize an objective function in this extended space, which can be characterized as consisting of infinite sequences of Radon measures which satisfy some constraints. Then, we approximate the action of the optimal sequence by that of a control, and finally develop numerical methods to estimate these nearly optimal controls. Several numerical examples are presented to illustrate these ideas.
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  • 93
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    Journal of optimization theory and applications 78 (1993), S. 523-539 
    ISSN: 1573-2878
    Keywords: Constrained control ; robust control ; periodic systems ; linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The problem is considered of finding a control strategy for a linear discrete-time periodic system with state and control bounds in the presence of unknown disturbances that are only known to belong to a given compact set. This kind of problem arises in practice in resource distribution systems where the demand has typically a periodic behavior, but cannot be estimated a priori without an uncertainty margin. An infinite-horizon keeping problem is formulated, which consists in confining the state within its constraint set using the allowable control, whatever the allowed disturbances may be. To face this problem, the concepts of periodically invariant set and sequence are introduced. They are used to formulate a solution strategy that solves the keeping problem. For the case of polyhedral state, control, and disturbance constraints, a computationally feasible procedure is proposed. In particular, it is shown that periodically invariant sequences may be computed off-line, and then they may be used to synthesize on-line a control strategy. Finally, an optimization criterion for the control law is discussed.
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  • 94
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    Journal of optimization theory and applications 80 (1994), S. 319-331 
    ISSN: 1573-2878
    Keywords: Predictor-corrector algorithm ; weighted center of the solution set ; linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract In certain applications of linear programming, the determination of a particular solution, the weighted center of the solution set, is often desired, giving rise to the need for algorithms capable of locating such center. In this paper, we modify the Mizuno-Todd-Ye predictor-corrector algorithm so that the modified algorithm is guaranteed to converge to the weighted center for given weights. The key idea is to ensure that iterates remain in a sequence of shrinking neighborhoods of the weighted central path. The modified algorithm also possesses polynomiality and superlinear convergence.
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  • 95
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    Journal of optimization theory and applications 84 (1995), S. 207-234 
    ISSN: 1573-2878
    Keywords: Pairwise comparisons ; eigenvectors ; analytic hierarchy process ; linear programming ; fuzzy sets ; membership values ; artificial intelligence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract One of the most difficult issues in many real-life decisionmaking problems is how to estimate the pertinent data. An approach which uses pairwise comparisons was proposed by Saaty and is widely accepted as an effective way of determining these data. Suppose that two matrices with pairwise comparisons are available. Furthermore, suppose that there is an overlapping of the elements compared in these two matrices. The problem examined in this paper is how to combine the comparisons of the two matrices in order to derive the priorities of the elements considered in both matrices. A simple approach and a linear programming approach are formulated and analyzed in solving this problem. Computational results suggest that the LP approach, under certain conditions, is an effective way for dealing with this problem. The proposed approach is of critical importance because it can also result in a reduction of the total required number of comparisons.
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  • 96
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    Journal of optimization theory and applications 90 (1996), S. 357-380 
    ISSN: 1573-2878
    Keywords: Polyhedral sets ; extreme points ; multivalued maps ; continuity ; stability ; linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper is focused on the stability properties of the extreme point set of a polyhedron. We consider a polyhedral setX(A,b) which is defined by a linear system of equality and inequality constraintsAx≤b, where the matrixA and the right-hand sideb are subject to perturbations. The extreme point setE(X(A,b)) of the polyhedronX(A,b) defines a multivalued map ℳ:(A,b)→E(X(A,b)). In the paper, characterization of continuity and Lipschitz continuity of the map ℳ is obtained. Boundedness of the setX(A,b) is not assumed It is shown that lower Lipschitz continuity is equivalent to the lower semicontinuity of the map ℳ and to the Robinson and Mangasarian-Fromovitz constraint qualifications. Upper Lipschitz continuity is proved to be equivalent to the upper semicontinuity of the map ℳ. It appears that the upper semicontinuity of the map ℳ implies the lower semicontinuity of this map. Some examples of using the conditions obtained are provided.
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  • 97
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    Journal of optimization theory and applications 91 (1996), S. 523-542 
    ISSN: 1573-2878
    Keywords: Duality theory ; shadow prices ; perturbations ; linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A systematic exposition of duality theory is given on what appears to be the optimal level of generality. A condition is offered which implies that the ideal of duality theory is achieved. For the case of linear programming, our approach leads to two novel features. In the first place, primal and dual LP-problems and complementarity conditions are defined canonically, without choosing a matrix form. In the second place, without deriving the explicit form of the dual problem, we show that the following well-known fact implies that the condition mentioned above holds: the polyhedral set property is invariant under linear maps. We give a new quick algorithmic proof of this fact.
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  • 98
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    Journal of optimization theory and applications 93 (1997), S. 619-634 
    ISSN: 1573-2878
    Keywords: Quadratic penalty functions ; linear programming ; piece-wise-linear path-following algorithms ; characterization of optimal solutions ; finiteness
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper takes a fresh look at the application of quadratic penalty functions to linear programming. Recently, Madsen et al. (Ref. 1) described a continuation algorithm for linear programming based on smoothing a dual l 1-formulation of a linear program with unit bounds. The present paper is prompted by the observation that this is equivalent to applying a quadratic penalty function to the dual of a linear program in standard canonical form, in the sense that both approaches generate continuous, piecewise-linear paths leading to the optimal solution set. These paths lead to new characterizations of optimal solutions in linear programming. An important product of this analysis is a finite penalty algorithm for linear programming closely related to the least-norm algorithm of Mangasarian (Ref. 2) and to the continuation algorithm of Madsen et al. (Ref. 1). The algorithm is implemented, and promising numerical results are given.
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  • 99
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    Journal of optimization theory and applications 93 (1997), S. 301-319 
    ISSN: 1573-2878
    Keywords: Convex sets ; polyhedral convex sets ; separation theorem ; Banach spaces ; duality theory ; weak topology ; linear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The subject of this paper is to study the problem of the minimum distance to the complement of a convex set. Nirenberg has stated a duality theorem treating the minimum norm problem for a convex set. We state a duality result which presents some analogy with the Nirenberg theorem, and we apply this result to polyhedral convex sets. First, we assume that the polyhedral set is expressed as the intersection of some finite collection of m given half-spaces. We show that a global solution is determined by solving m convex programs. If the polyhedral set is expressed as the convex hull of a given finite set of extreme points, we show that a global minimum for a polyhedral norm is obtained by solving a finite number of linear programs.
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  • 100
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    Acta mathematicae applicatae sinica 12 (1996), S. 1-10 
    ISSN: 1618-3932
    Keywords: Neural network ; linear programming ; quadratic programming ; penalty function method
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Artificial neural network techniques have been introduced into the area of optimization in the recent decade. Some neural network models have been suggested to solve linear and quadratic programming problems. The Kennedy and Chua model[5] is one of these networks. In this paper results about the convergence of the model are obtained. Another related problem is how to choose a parameter value $$\tilde s$$ so that the equilibrium point of the network immediately and properly approximates the original solution. Such an estimation for the parameter is given in a closed form when the network is used to solve linear programming.
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