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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 58 (1993), S. 243-255 
    ISSN: 1436-4646
    Keywords: Linear programming ; interior point algorithm ; primal—dual potential function
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper is concerned with selection of theρ-parameter in the primal—dual potential reduction algorithm for linear programming. Chosen from [n + $$\sqrt n $$ , ∞), the level ofρ determines the relative importance placed on the centering vs. the Newton directions. Intuitively, it would seem that as the iterate drifts away from the central path towards the boundary of the positive orthant,ρ must be set close ton + $$\sqrt n $$ . This increases the relative importance of the centering direction and thus helps to ensure polynomial convergence. In this paper, we show that this is unnecessary. We find for any iterate thatρ can be sometimes chosen in a wide range [n + $$\sqrt n $$ , ∞) while still guaranteeing the currently best convergence rate of O( $$\sqrt n $$ L) iterations. This finding is encouraging since in practice large values ofρ have resulted in fast convergence rates. Our finding partially complements the recent result of Zhang, Tapia and Dennis (1990) concerning the local convergence rate of the algorithm.
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  • 2
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    Mathematical programming 59 (1993), S. 133-150 
    ISSN: 1436-4646
    Keywords: Linear programming ; interior-point methods ; combined phase I—phase II
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper describes an affine potential reduction algorithm for linear programming that simultaneously seeks feasibility and optimality. The algorithm is closely related to a similar method of Anstreicher. The new features are that we use a two-dimensional programming problem to derive better lower bounds than Anstreicher, that our direction-finding subproblem treats phase I and phase II more symmetrically, and that we do not need an initial lower bound. Our method also allows for the generation of a feasible solution (so that phase I is terminated) during the course of the iterations, and we describe two ways to encourage this behavior.
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  • 3
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    Mathematical programming 59 (1993), S. 151-162 
    ISSN: 1436-4646
    Keywords: Linear programming ; primal and dual ; superlinear and quadratic convergence ; polynomiality
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Recently, Ye, Tapia and Zhang (1991) demonstrated that Mizuno—Todd—Ye's predictor—corrector interior-point algorithm for linear programming maintains the O( $$\sqrt n $$ L)-iteration complexity while exhibiting superlinear convergence of the duality gap to zero under the assumption that the iteration sequence converges, and quadratic convergence of the duality gap to zero under the assumption of nondegeneracy. In this paper we establish the quadratic convergence result without any assumption concerning the convergence of the iteration sequence or nondegeneracy. This surprising result, to our knowledge, is the first instance of a demonstration of polynomiality and superlinear (or quadratic) convergence for an interior-point algorithm which does not assume the convergence of the iteration sequence or nondegeneracy.
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  • 4
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    Mathematical programming 59 (1993), S. 413-420 
    ISSN: 1436-4646
    Keywords: Linear programming ; prize collecting ; rounding fractional solutions ; traveling salesman problem ; worst-case analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We study the version of the prize collecting traveling salesman problem, where the objective is to find a tour that visits a subset of vertices such that the length of the tour plus the sum of penalties associated with vertices not in the tour is as small as possible. We present an approximation algorithm with constant bound. The algorithm is based on Christofides' algorithm for the traveling salesman problem as well as a method to round fractional solutions of a linear programming relaxation to integers, feasible for the original problem.
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  • 5
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    Mathematical programming 62 (1993), S. 517-535 
    ISSN: 1436-4646
    Keywords: Linear programming ; Karmarkar's algorithm ; Projective algorithm ; Standard form
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In a recent paper, Shaw and Goldfarb show that a version of the standard form projective algorithm can achieve $$O\left( {\sqrt {nL} } \right)$$ step complexity, as opposed to the O(nL) step complexity originally demonstrated for the algorithm. The analysis of Shaw and Goldfarb shows that the algorithm, using a constant, fixed steplength, approximately follows the central trajectory. In this paper we show that simple modifications of the projective algorithm obtain the same complexity improvement, while permitting a linesearch of the potential function on each step. An essential component is the addition of a single constraint, motivated by Shaw and Goldfarb's analysis, which makes the standard form algorithm strictly monotone in the true objective.
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  • 6
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    European journal of nutrition 32 (1993), S. 79-92 
    ISSN: 1436-6215
    Keywords: Lineare Programmierung ; Ernährungsoptimierung ; Privathaushalt ; Optimierungsmodelle ; Linear programming ; nutrition optimization ; private households ; optimization models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition , Medicine
    Description / Table of Contents: Summary Members of private households have different nutrient requirements. In general, they eat the same dishes in different quantities. The menu plan is admissible if it corresponds to the recommended dietary allowances. It is acceptable if it meets the eating habits. It is optimal if it meets the constraints mentioned and best reaches an objective. It is the aim of this paper to describe models for the determination of optimal nutrition and to evaluate them with respect to their suitability for solving decision problems in private, multi-person-households. The fewer the model-intern restrictions in the variability of quantities of food stuffs, kind and/or quantities of dishes, the better are the “optimal” solutions that are found with the model. A simultaneous determination of kind and quantity of dishes reaches the model purpose better than a stepwise determination. This is shown in an example problem.
    Notes: Zusammenfassung In privaten Haushalten haben die Mitglieder unterschiedliche Nährstoffbedarfe. Sie verzehren im allgemeinen gleiche Speisen in unterschiedlichen Mengen. Der Speisenplan ist bedarfsgerecht, wenn er den Empfehlungen für die Nährstoffzufuhr der einzelnen Personen entspricht. Er ist akzeptabel, wenn er den Verzehrgewohnheiten der Personen entspricht. Er ist optimal, wenn er die genannten Bedingungen einhält und zusätzlich ein gegebenes Ziel bestmöglich erreicht. Die Bestimmung eines optimalen Speisenplans erfolgt anhand von Modellen. Es ist das Ziel des Beitrags, verschiedene Modelle zur Bestimmung einer optimalen Ernährung darzustellen und im Hinblick auf ihre Eignung zur Anwendung auf Entscheidungsprobleme im privaten Mehrpersonenhaushalt zu beurteilen. Je geringer die modellinterne Einschränkung in der Variabilität von Lebensmittelmengen, Speisenarten und/oder Speisenmengen, desto bessere ‚optimale‘ Lösungen können mit dem Modell gefunden werden. Eine simultane Bestimmung von Speisenart und -menge erfüllt den Modellzweck besser als eine sukzessive Bestimmung. Dies konnte anhand eines Beispielproblems gezeigt werden.
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  • 7
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    Mathematical programming 59 (1993), S. 23-31 
    ISSN: 1436-4646
    Keywords: Linear programming ; duality theorem ; unimodular ; totally unimodular ; interior point methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper we consider a linear programming problem with the underlying matrix unimodular, and the other data integer. Given arbitrary near optimum feasible solutions to the primal and the dual problems, we obtain conditions under which statements can be made about the value of certain variables in optimal vertices. Such results have applications to the problem of determining the stopping criterion in interior point methods like the primal—dual affine scaling method and the path following methods for linear programming.
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  • 8
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    Annals of operations research 46-47 (1993), S. 409-430 
    ISSN: 1572-9338
    Keywords: Linear programming ; Phase I ; nonlinear programming ; least squares ; quadratic programming ; strict improvement ; degeneracy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract Instead of trying to recognize and avoid degenerate steps in the simplex method (as some variants do), we have developed a new Phase I algorithm that is impervious to degeneracy. The new algorithm solves a non-negative least-squares problem in order to find a Phase I solution. In each iteration, a simple two-variable least-squares subproblem is used to select an incoming column to augment a set of independent columns (called “basic”) to get a strictly better fit to the right-hand side. Although this is analogous in many ways to the simplex method, it can be proved that strict improvement is attained at each iteration, even in the presence of degeneracy. Thus cycling cannot occur, and convergence is guaranteed. This algorithm is closely related to a number of existing algorithms proposed for non-negative least-squares and quadratic programs. When used on the 30 smallest NETLIB linear programming test problems, the computational results for the new Phase I algorithm were almost 3.5 times faster than a particular implementation of the simplex method; on some problems, it was over 10 times faster. Best results were generally seen on the more degenerate problems.
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  • 9
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    Annals of operations research 46-47 (1993), S. 107-138 
    ISSN: 1572-9338
    Keywords: Linear programming ; interior point methods ; degeneracy ; polynomial algorithms ; global and local convergence ; basis recovery ; numerical performance ; sensitivity analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract The publication of Karmarkar's paper has resulted in intense research activity into Interior Point Methods (IPMs) for linear programming. Degeneracy is present in most real-life problems and has always been an important issue in linear programming, especially in the Simplex method. Degeneracy is also an important issue in IPMs. However, the difficulties are different in the two methods. In this paper, we survey the various theoretical and practical issues related to degeneracy in IPMs for linear programming. We survey results, which, for the most part, have already appeared in the literature. Roughly speaking, we shall deal with the effect of degeneracy on the following: the convergence of IPMs, the trajectories followed by the algorithms, numerical performance, and finding basic solutions.
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  • 10
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    Annals of operations research 46-47 (1993), S. 235-248 
    ISSN: 1572-9338
    Keywords: Linear programming ; generalized networks ; simplex method ; degeneracy ; lexicography ; cycling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract This paper introduces an analytical approach for studying lexicography in generalized network problems. The equations obtained can help us to understand and to extend the existing theory. First, it is verified that all nonzero elements have the same sign in each row vector of a basis inverse for a generalized network (GN) problem with positive multipliers. However, this property does not necessarily hold when there exist negative multipliers. Second, we developed a strategy to select the dropping arc in the GN simplex algorithm when addressing GN problems with positive andnegative multipliers. This strategy is also based on lexicography and requires performing some comparisons. However, the values to be compared are already known since they can be obtained as a by-product of the calculations necessary to compute the basis representation of the entering arc. Consequently, the computational effort per pivot step isO(n) in the worst case. This worst case effort is the same as that required by the strongly convergent rules for selecting the dropping arc in the method of strong convergence.
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  • 11
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    Annals of operations research 46-47 (1993), S. 203-233 
    ISSN: 1572-9338
    Keywords: Linear programming ; simplex method ; pivot rules ; cycling ; recursion ; minimal index rule ; parametric programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract The purpose of this paper is to discuss the various pivot rules of the simplex method and its variants that have been developed in the last two decades, starting from the appearance of the minimal index rule of Bland. We are mainly concerned with finiteness properties of simplex type pivot rules. Well known classical results concerning the simplex method are not considered in this survey, but the connection between the new pivot methods and the classical ones, if there is any, is discussed. In this paper we discuss three classes of recently developed pivot rules for linear programming. The first and largest class is the class of essentially combinatorial pivot rules including minimal index type rules and recursive rules. These rules only use labeling and signs of the variables. The second class contains those pivot rules which can actually be considered as variants or generalizations or specializations of Lemke's method, and so they are closely related to parametric programming. The last class has the common feature that the rules all have close connections to certain interior point methods. Finally, we mention some open problems for future research.
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  • 12
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    Annals of operations research 46-47 (1993), S. 431-442 
    ISSN: 1572-9338
    Keywords: Linear programming ; degeneracy ; network simplex algorithm ; pivoting ; minimal cost network flow
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract A characteristic feature of the primal network simplex algorithm (NSA) is that it usually makes a large number of degenerate iterations. Though cycling and even stalling can be avoided by recently introduced pivot rules for NSA, the practical efficiency of these rules is not known yet. For the case when the simplex algorithm is used to solve the continuous linear programming (LP) problem there exists a practical anti-cycling procedure that proved to be efficient. It is based on an expanding relaxation of the individual bound on the variables. In this paper we discuss the adaptation of this method to NSA, taking advantage of the special integer nature of network problems. We also give an account of our experience with these ideas as they are experimentally implemented in the MINET network LP solver. Reductions of CPU time have been achieved on a smaller set of specially structured real-life problems.
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  • 13
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    Mathematical programming 59 (1993), S. 345-360 
    ISSN: 1436-4646
    Keywords: Linear programming ; interior point method ; active set strategy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We will present a potential reduction method for linear programming where only the constraints with relatively small dual slacks—termed “active constraints”—will be taken into account to form the ellipsoid constraint at each iteration of the process. The algorithm converges to the optimal feasible solution in O( $$\sqrt n $$ L) iterations with the same polynomial bound as in the full constraints case, wheren is the number of variables andL is the data length. If a small portion of the constraints is active near the optimal solution, the computational cost to find the next direction of movement in one iteration may be considerably reduced by the proposed strategy.
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  • 14
    ISSN: 1436-4646
    Keywords: Linear programming ; quadratic programming ; convex programming ; randomized algorithms ; fixed dimension optimization problems ; complexity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We extend Clarkson's randomized algorithm for linear programming to a general scheme for solving convex optimization problems. The scheme can be used to speed up existing algorithms on problems which have many more constraints than variables. In particular, we give a randomized algorithm for solving convex quadratic and linear programs, which uses that scheme together with a variant of Karmarkar's interior point method. For problems withn constraints,d variables, and input lengthL, ifn = Ω(d 2), the expected total number of major Karmarkar's iterations is O(d 2(logn)L), compared to the best known deterministic bound of O( $$\sqrt n$$ L). We also present several other results which follow from the general scheme.
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  • 15
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    Mathematical programming 62 (1993), S. 41-67 
    ISSN: 1436-4646
    Keywords: Linear programming ; Dantzig—Wolfe decomposition ; large-scale systems ; parallel processing ; hypercube architecture
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Decomposition algorithms for block-angular linear programs give rise to a natural, coarse-grained parallelism that can be exploited by processing the subproblems concurrently within a distributed-memory environment. The parallel efficiency of the distributed approach, however, is critically dependent on the duration of the inherently serial master phase relative to that of the bottleneck subproblem. This paper investigates strategies for improving efficiency in distributed Dantzig—Wolfe decomposition by better balancing the load between the master and subproblem processors. We report computational experience on an Intel iPSC/2 hypercube multiprocessor with test problems having dimensions up to about 30 000 rows, 87 000 columns, and 200 coupling constraints.
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  • 16
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    Mathematical programming 62 (1993), S. 15-39 
    ISSN: 1436-4646
    Keywords: Linear programming ; interior-point methods ; symmetric indefinite systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We describe an implementation of a primal—dual path following method for linear programming that solves symmetric indefinite “augmented” systems directly by Bunch—Parlett factorization, rather than reducing these systems to the positive definite “normal equations” that are solved by Cholesky factorization in many existing implementations. The augmented system approach is seen to avoid difficulties of numerical instability and inefficiency associated with free variables and with dense columns in the normal equations approach. Solving the indefinite systems does incur an extra overhead, whose median is about 40% in our tests; but the augmented system approach proves to be faster for a minority of cases in which the normal equations have relatively dense Cholesky factors. A detailed analysis shows that the augmented system factorization is reliable over a fairly large range of the parameter settings that control the tradeoff between sparsity and numerical stability.
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  • 17
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    Mathematical programming 62 (1993), S. 119-131 
    ISSN: 1436-4646
    Keywords: Linear programming ; interior point algorithm ; complexity ; potential function
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We propose a potential-reduction algorithm which always uses the primal—dual affine-scaling direction as a search direction. We choose a step size at each iteration of the algorithm such that the potential function does not increase, so that we can take a longer step size than the minimizing point of the potential function. We show that the algorithm is polynomial-time bounded. We also propose a low-complexity algorithm, in which the centering direction is used whenever an iterate is far from the path of centers.
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  • 18
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    Mathematical programming 62 (1993), S. 497-515 
    ISSN: 1436-4646
    Keywords: Linear programming ; primal—dual methods ; optimal face ; strict complementarity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We study the problem of finding a point in the relative interior of the optimal face of a linear program. We prove that in the worst case such a point can be obtained in O(n 3 L) arithmetic operations. This complexity is the same as the complexity for solving a linear program. We also show how to find such a point in practice. We report and discuss computational results obtained for the linear programming problems in the NETLIB test set.
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  • 19
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    Algorithmica 9 (1993), S. 64-83 
    ISSN: 1432-0541
    Keywords: Linear programming ; Interior-point methods ; Projective methods ; Combined phase 1-phase 2
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We compare the projective methods for linear programming due to de Ghellinck and Vial, Anstreicher, Todd, and Fraley. These algorithms have the feature that they approach feasibility and optimality simultaneously, rather than requiring an initial feasible point. We compare the directions used in these methods and the lower-bound updates employed. In many cases the directions coincide and two of the lower-bound updates give the same result. It appears that Todd's direction and Fraley's lower-bound update have slight advantages, and this is borne out in limited computational testing.
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  • 20
    ISSN: 1432-0541
    Keywords: Linear programming ; Karmarkar's algorithm ; Potential function ; Primal-dual, Modified method ; Rank-one updates
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We consider partial updating in Kojima, Mizuno, and Yoshise's primal-dual potential reduction algorithm for linear programming. We use a simple safeguard condition to control the number of updates incurred on combined primal-dual steps. Our analysis allows for unequal steplengths in the primal and dual variables, which appears to be a computationally significant factor for primal-dual methods. The safeguard we use is a primal-dual Goldstein-Armijo condition, modified to deal with the unequal primal and dual steplengths.
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