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  • Articles  (56,131)
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  • 1
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    Springer
    Mathematical programming 49 (1990), S. 163-187 
    ISSN: 1436-4646
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A commonly studied relaxation of the travelling salesman problem is obtained by adding subtour elimination constraints to the constraints of a 2-factor problem and removing the integrality requirement. We investigate the problem of solving this relaxation for a special type of objective function. We also discuss some ways in which this relates to the concept of rank introduced by Chvátal.
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  • 2
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    Mathematical programming 46 (1990), S. 1-29 
    ISSN: 1436-4646
    Keywords: Global optimization ; concurrent ; parallel ; stochastic ; network of computers
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    Topics: Computer Science , Mathematics
    Notes: Abstract The global optimization problem, finding the lowest minimizer of a nonlinear function of several variables that has multiple local minimizers, appears well suited to concurrent computation. This paper presents a new parallel algorithm for the global optimization problem. The algorithm is a stochastic method related to the multi-level single-linkage methods of Rinnooy Kan and Timmer for sequential computers. Concurrency is achieved by partitioning the work of each of the three main parts of the algorithm, sampling, local minimization start point selection, and multiple local minimizations, among the processors. This parallelism is of a coarse grain type and is especially well suited to a local memory multiprocessing environment. The paper presents test results of a distributed implementation of this algorithm on a local area network of computer workstations. It also summarizes the theoretical properties of the algorithm.
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  • 3
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    Mathematical programming 46 (1990), S. 105-122 
    ISSN: 1436-4646
    Keywords: Nondifferentiable minimization ; convex programming ; numerical methods ; descent methods
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    Topics: Computer Science , Mathematics
    Notes: Abstract Proximal bundle methods for minimizing a convex functionf generate a sequence {x k } by takingx k+1 to be the minimizer of $$\hat f^k (x) + u^k |x - x^k |^2 /2$$ , where $$\hat f^k $$ is a sufficiently accurate polyhedral approximation tof andu k 〉 0. The usual choice ofu k = 1 may yield very slow convergence. A technique is given for choosing {u k } adaptively that eliminates sensitivity to objective scaling. Some encouraging numerical experience is reported.
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  • 4
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    Mathematical programming 46 (1990), S. 127-151 
    ISSN: 1436-4646
    Keywords: Dual descent ; monotropic program ; Tucker tableau ; elementary vector
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    Topics: Computer Science , Mathematics
    Notes: Abstract We propose a dual descent method for the problem of minimizing a convex, possibly nondifferentiable, separable cost subject to linear constraints. The method has properties reminiscent of the Gauss-Seidel method in numerical analysis and uses theε-complementary slackness mechanism introduced in Bertsekas, Hosein and Tseng (1987) to ensure finite convergence to near optimality. As special cases we obtain the methods in Bertsekas, Hosein and Tseng (1987) for network flow programs and the methods in Tseng and Bertsekas (1987) for linear programs.
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  • 5
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    Mathematical programming 46 (1990), S. 219-224 
    ISSN: 1436-4646
    Keywords: Algebraic optimization ; location theory ; ellipsoid algorithms
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    Notes: Abstract The Fermat-Weber location problem is to find a point in ℝ n that minimizes the sum of the (weighted) Euclidean distances fromm given points in ℝ n . In this work we discuss some relevant complexity and algorithmic issues. First, using Tarski's theory on solvability over real closed fields we argue that there is an infinite scheme to solve the problem, where the rate of convergence is equal to the rate of the best method to locate a real algebraic root of a one-dimensional polynomial. Secondly, we exhibit an explicit solution to the strong separation problem associated with the Fermat-Weber model. This separation result shows that anε-approximation solution can be constructed in polynomial time using the standard Ellipsoid Method.
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  • 6
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    Mathematical programming 46 (1990), S. 255-256 
    ISSN: 1436-4646
    Keywords: Linear complementarity problem ; P-matrix
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    Topics: Computer Science , Mathematics
    Notes: Abstract We give a short proof of the finiteness of Murty's principal pivoting algorithm for solving the linear complementarity problemy = Mz + q, y T z = 0,y ≥ 0,z ≥ 0 withP-matrixM.
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  • 7
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    Mathematical programming 47 (1990), S. 305-336 
    ISSN: 1436-4646
    Keywords: Trust region ; linear constraints ; convex constraints ; global convergence ; local convergence ; degeneracy ; rate of convergence ; identification of active constraints ; Newton's method ; sequential quadratic programming ; gradient projection
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    Notes: Abstract We develop a convergence theory for convex and linearly constrained trust region methods which only requires that the step between iterates produce a sufficient reduction in the trust region subproblem. Global convergence is established for general convex constraints while the local analysis is for linearly constrained problems. The main local result establishes that if the sequence converges to a nondegenerate stationary point then the active constraints at the solution are identified in a finite number of iterations. As a consequence of the identification properties, we develop rate of convergence results by assuming that the step is a truncated Newton method. Our development is mainly geometrical; this approach allows the development of a convergence theory without any linear independence assumptions.
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  • 8
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    Mathematical programming 47 (1990), S. 425-439 
    ISSN: 1436-4646
    Keywords: Isotonic regression ; active sets
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    Topics: Computer Science , Mathematics
    Notes: Abstract In this and subsequent papers we will show that several algorithms for the isotonic regression problem may be viewed as active set methods. The active set approach provides a unifying framework for studying algorithms for isotonic regression, simplifies the exposition of existing algorithms and leads to several new efficient algorithms. We also investigate the computational complexity of several algorithms. In this paper we consider the isotonic regression problem with respect to a complete order $$\begin{gathered} minimize\sum\limits_{i = 1}^n {w_i } (y_i - x_i )^2 \hfill \\ subject tox_1 \leqslant x_2 \leqslant \cdot \cdot \cdot \leqslant x_n \hfill \\ \end{gathered} $$ where eachw i is strictly positive and eachy i is an arbitrary real number. We show that the Pool Adjacent Violators algorithm (due to Ayer et al., 1955; Miles, 1959; Kruskal, 1964), is a dual feasible active set method and that the Minimum Lower Set algorithm (due to Brunk et al., 1957) is a primal feasible active set method of computational complexity O(n 2). We present a new O(n) primal feasible active set algorithm. Finally we discuss Van Eeden's method and show that it is of worst-case exponential time complexity.
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  • 9
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    Mathematical programming 47 (1990), S. 463-463 
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  • 10
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    Mathematical programming 48 (1990), S. 1-17 
    ISSN: 1436-4646
    Keywords: Coercivity ; quadratic programming ; linear programming ; aggregation ; relaxation ; multigrid methods
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    Notes: Abstract This paper is concerned with multilevel iterative methods which combine a descent scheme with a hierarchy of auxiliary problems in lower dimensional subspaces. The construction of auxiliary problems as well as applications to elasto-plastic model and linear programming are described. The auxiliary problem for the dual of a perturbed linear program is interpreted as a dual of perturbed aggregated linear program. Coercivity of the objective function over the feasible set is sufficient for the boundedness of the iterates. Equivalents of this condition are presented in special cases.
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  • 11
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    Mathematical programming 49 (1990), S. 91-111 
    ISSN: 1436-4646
    Keywords: Sparse matrices ; linear programming ; bipartite matching
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    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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  • 12
    ISSN: 1436-4646
    Keywords: Quasi-Newton methods ; collinear scalings ; conic approximations ; local and q-superlinear convergence
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    Notes: Abstract This paper is concerned with collinear scaling algorithms for unconstrained minimization where the underlying local approximants are forced to interpolate the objective function value and gradient at only the two most recent iterates. By suitably modifying the procedure of Sorensen (1980) for deriving such algorithms, we show that two members of the algorithm class derived related to the DFP and BFGS methods respectively are locally and q-superlinearly convergent. This local analysis as well as the results they yield exhibit the same sort of “duality” exhibited by those of Broyden, Dennis and Moré (1973) and Dennis and Moré (1974) for the DFP and BFGS methods. The results in this paper also imply the local and q-superlinear convergence of collinear scaling algorithms of Sorensen (1982, pp. 154–156) related to the DFP and BFGS methods.
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  • 13
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    Mathematical programming 49 (1990), S. 139-141 
    ISSN: 1436-4646
    Keywords: Linear complementarity problem ; copositive ; strictly copositive ; Q-matrix
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    Topics: Computer Science , Mathematics
    Notes: Abstract Recently, Jeter and Pye gave an example to show that Pang's conjecture, thatL 1 ⋂Q $$ \subseteq R_0 $$ , is false. We show in this article that the above conjecture is true for symmetric matrices. Specifically, we show that a symmetric copositive matrix is inQ if and only if it is strictly copositive.
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  • 14
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    Mathematical programming 49 (1990), S. 145-162 
    ISSN: 1436-4646
    Keywords: Linear programming ; interior-point methods ; primal—dual algorithms ; feasibility
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    Notes: Abstract A new method for obtaining an initial feasible interior-point solution to a linear program is presented. This method avoids the use of a “big-M”, and is shown to work well on a standard set of test problems. Conditions are developed for obtaining a near-optimal solution that is feasible for an associated problem, and details of the computational testing are presented. Other issues related to obtaining and maintaining accurate feasible solutions to linear programs with an interior-point method are discussed. These issues are important to consider when solving problems that have no primal or dual interior-point feasible solutions.
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  • 15
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    Mathematical programming 49 (1990), S. 231-251 
    ISSN: 1436-4646
    Keywords: Nonsmooth optimization ; subgradient ; ε-subgradient ; exact penalty function ; successive quadratic programming
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    Notes: Abstract In this paper we present an algorithm for solving nonlinear programming problems where the objective function contains a possibly nonsmooth convex term. The algorithm successively solves direction finding subproblems which are quadratic programming problems constructed by exploiting the special feature of the objective function. An exact penalty function is used to determine a step-size, once a search direction thus obtained is judged to yield a sufficient reduction in the penalty function value. The penalty parameter is adjusted to a suitable value automatically. Under appropriate assumptions, the algorithm is shown to produce an approximate optimal solution to the problem with any desirable accuracy in a finite number of iterations.
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  • 16
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    Mathematical programming 49 (1990), S. 325-340 
    ISSN: 1436-4646
    Keywords: Convex quadratic programming ; interior point method ; Karmarkar's method ; logarithmic barrier function method
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    Notes: Abstract We present a primal interior point method for convex quadratic programming which is based upon a logarithmic barrier function approach. This approach generates a sequence of problems, each of which is approximately solved by taking a single Newton step. It is shown that the method requires $$O(\sqrt n L)$$ iterations and O(n 3.5 L) arithmetic operations. By using modified Newton steps the number of arithmetic operations required by the algorithm can be reduced to O(n 3 L).
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  • 17
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    Mathematical programming 49 (1990), S. 371-379 
    ISSN: 1436-4646
    Keywords: Z-function ; strong solvability with nonnegativity ; strictly semimonotone function
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    Notes: Abstract This paper focuses on the relationship between the ‘strong’ solvability of a certain system involving a Z-function, and the strict semimonotonicity of such a function. Our main result shows that, for a system defined by a continuous, superhomogeneous Z-function, the additional condition of strict semimonotonicity is both necessary and sufficient for strong solvability.
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  • 18
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    Mathematical programming 49 (1990), S. 397-411 
    ISSN: 1436-4646
    Keywords: Concave functions ; knapsack problems ; strict minimizers ; NP-hard ; nonconvex ; local minimizers
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    Topics: Computer Science , Mathematics
    Notes: Abstract We consider a version of the knapsack problem which gives rise to a separable concave minimization problem subject to bounds on the variables and one equality constraint. We characterize strict local miniimizers of concave minimization problems subject to linear constraints, and use this characterization to show that although the problem of determining a global minimizer of the concave knapsack problem is NP-hard, it is possible to determine a local minimizer of this problem with at most O(n logn) operations and 1+[logn] evaluations of the function. If the function is quadratic this algorithm requires at most O(n logn) operations.
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  • 19
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    Mathematical programming 58 (1993), S. 53-88 
    ISSN: 1436-4646
    Keywords: Symmetric traveling salesman problem ; graphical traveling salesman problem ; polyhedron ; facet ; linear inequality ; lifting ; composition of inequalities
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    Notes: Abstract A present trend in the study of theSymmetric Traveling Salesman Polytope (STSP(n)) is to use, as a relaxation of the polytope, thegraphical relaxation (GTSP(n)) rather than the traditionalmonotone relaxation which seems to have attained its limits. In this paper, we show the very close relationship between STSP(n) and GTSP(n). In particular, we prove that every non-trivial facet of STSP(n) is the intersection ofn + 1 facets of GTSP(n),n of which are defined by the degree inequalities. This fact permits us to define a standard form for the facet-defining inequalities for STSP(n), that we calltight triangular, and to devise a proof technique that can be used to show that many known facet-defining inequalities for GTSP(n) define also facets of STSP(n). In addition, we give conditions that permit to obtain facet-defining inequalities by composition of facet-defining inequalities for STSP(n) and general lifting theorems to derive facet-defining inequalities for STSP(n +k) from inequalities defining facets of STSP(n).
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  • 20
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    Mathematical programming 58 (1993), S. 111-136 
    ISSN: 1436-4646
    Keywords: ABS algorithms ; linear least squares ; overdetermined linear systems ; QR factorization ; Gram–Schmidt algorithm ; numerical experiments
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    Notes: Abstract The ABS class for linear and nonlinear systems has been recently introduced by Abaffy, Broyden, Galantai and Spedicato. Here we consider various ways of applying these algorithms to the determination of the minimal euclidean norm solution of over-determined linear systems in the least squares sense. Extensive numerical experiments show that the proposed algorithms are efficient and that one of them usually gives better accuracy than standard implementations of the QR orthogonalization algorithm with Householder reflections.
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  • 21
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    Mathematical programming 58 (1993), S. 243-255 
    ISSN: 1436-4646
    Keywords: Linear programming ; interior point algorithm ; primal—dual potential function
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    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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  • 22
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    Mathematical programming 58 (1993), S. 295-324 
    ISSN: 1436-4646
    Keywords: Cutting planes ; projection ; mixed 0–1 programming ; disjunctive programming
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    Notes: Abstract We propose a cutting plane algorithm for mixed 0–1 programs based on a family of polyhedra which strengthen the usual LP relaxation. We show how to generate a facet of a polyhedron in this family which is most violated by the current fractional point. This cut is found through the solution of a linear program that has about twice the size of the usual LP relaxation. A lifting step is used to reduce the size of the LP's needed to generate the cuts. An additional strengthening step suggested by Balas and Jeroslow is then applied. We report our computational experience with a preliminary version of the algorithm. This approach is related to the work of Balas on disjunctive programming, the matrix cone relaxations of Lovász and Schrijver and the hierarchy of relaxations of Sherali and Adams.
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    Mathematical programming 58 (1993), S. 429-431 
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    Mathematical programming 58 (1993), S. 433-434 
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    Mathematical programming 59 (1993), S. 1-21 
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    Keywords: Primal—dual interior point algorithm ; linear program ; large step ; global convergence ; polynomial-time convergence
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    Notes: Abstract This paper proposes two sets of rules, Rule G and Rule P, for controlling step lengths in a generic primal—dual interior point method for solving the linear programming problem in standard form and its dual. Theoretically, Rule G ensures the global convergence, while Rule P, which is a special case of Rule G, ensures the O(nL) iteration polynomial-time computational complexity. Both rules depend only on the lengths of the steps from the current iterates in the primal and dual spaces to the respective boundaries of the primal and dual feasible regions. They rely neither on neighborhoods of the central trajectory nor on potential function. These rules allow large steps without performing any line search. Rule G is especially flexible enough for implementation in practically efficient primal—dual interior point algorithms.
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    Mathematical programming 59 (1993), S. 71-85 
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    Notes: Abstract The strategy of Restricted Simplicial Decomposition is extended to convex programs with convex constraints. The resulting algorithm can also be viewed as an extension of the (scaled) Topkis—Veinott method of feasible directions in which the master problem involves optimization over a simplex rather than the usual line search. Global convergence of the method is proven and conditions are given under which the master problem will be solved a finite number of times. Computational testing with dense quadratic problems confirms that the method dramatically improves the Topkis—Veinott algorithm and that it is competitive with the generalized reduced gradient method.
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    Mathematical programming 59 (1993), S. 133-150 
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    Keywords: Linear programming ; interior-point methods ; combined phase I—phase II
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    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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    Mathematical programming 59 (1993), S. 151-162 
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    Keywords: Linear programming ; primal and dual ; superlinear and quadratic convergence ; polynomiality
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    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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    Mathematical programming 59 (1993), S. 87-115 
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    Keywords: Graph partition ; multiway cut ; polytope ; facet
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    Notes: Abstract In this paper we describe several forms of thek-partition problem and give integer programming formulations of each case. The dimension of the associated polytopes and some basic facets are identified. We also give several valid and facet defining inequalities for each of the polytopes.
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    Mathematical programming 59 (1993), S. 33-48 
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    Keywords: 90C33 ; Linear complementary problems ; iterative methods ; quadratic programming
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    Notes: Abstract We prove convergence of the whole sequence generated by any of a large class of iterative algorithms for the symmetric linear complementarity problem (LCP), under the only hypothesis that a quadratic form associated with the LCP is bounded below on the nonnegative orthant. This hypothesis holds when the matrix is strictly copositive, and also when the matrix is copositive plus and the LCP is feasible. The proof is based upon the linear convergence rate of the sequence of functional values of the quadratic form. As a by-product, we obtain a decomposition result for copositive plus matrices. Finally, we prove that the distance from the generated sequence to the solution set (and the sequence itself, if its limit is a locally unique solution) have a linear rate of R-convergence.
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    Mathematical programming 50 (1991), S. 91-98 
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    Keywords: 65K10 ; 62E15 ; 60F99 ; Random optimization ; expected length
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    Notes: Abstract When differentiability is not assumed random procedures can be successfully used to estimate the extreme values of a given function. For a class of such algorithms we treat the problem of estimating the mean effort.
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    Mathematical programming 59 (1993), S. 413-420 
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    Keywords: Linear programming ; prize collecting ; rounding fractional solutions ; traveling salesman problem ; worst-case analysis
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    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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    Mathematical programming 50 (1991), S. 29-51 
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    Keywords: Interior-point methods ; linear programming ; Karmarkar's algorithm ; logarithmic barrier function ; affine scaling algorithms ; continuous trajectories for linear programming
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    Notes: Abstract We consider the continuous trajectories of the vector field induced by the primal affine scaling algorithm as applied to linear programming problems in standard form. By characterizing these trajectories as solutions of certain parametrized logarithmic barrier families of problems, we show that these trajectories tend to an optimal solution which in general depends on the starting point. By considering the trajectories that arise from the Lagrangian multipliers of the above mentioned logarithmic barrier families of problems, we show that the trajectories of the dual estimates associated with the affine scaling trajectories converge to the so called ‘centered’ optimal solution of the dual problem. We also present results related to asymptotic direction of the affine scaling trajectories. We briefly discuss how to apply our results to linear programs formulated in formats different from the standard form. Finally, we extend the results to the primal-dual affine scaling algorithm.
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    Mathematical programming 50 (1991), S. 75-80 
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    Keywords: Subgradient method ; ε-subgradient ; nondifferentiable optimization
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    Notes: Abstract A generalized subgradient method is considered which uses the subgradients at previous iterations as well as the subgradient at current point. This method is a direct generalization of the usual subgradient method. We provide two sets of convergence conditions of the generalized subgradient method. Our results provide a larger class of sequences which converge to a minimum point and more freedom of adjustment to accelerate the speed of convergence.
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    Mathematical programming 60 (1993), S. 1-19 
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    Keywords: Convex programming ; linear programming ; multiplier method ; exponential penalty ; Augmented Lagrangian
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    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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    Mathematical programming 60 (1993), S. 93-113 
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    Keywords: 26B25 ; 90C20 ; Quadratic functions ; quasiconvexity ; subdifferentiability ; cutting plane methods
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    Notes: Abstract In this paper we characterize those quadratic functions whose restrictions to a convex set are boundedly lower subdifferentiable and, for the case of closed hyperbolic convex sets, those which are lower subdifferentiable but not boundedly lower subdifferentiable. Once characterized, we will study the applicability of the cutting plane algorithm of Plastria to problems where the objective function is quadratic and boundedly lower subdifferentiable.
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    Mathematical programming 60 (1993), S. 21-52 
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    Keywords: Vehicle routing ; polyhedron ; facet ; branch and cut
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    Notes: Abstract The capacitated vehicle routing problem (CVRP) considered in this paper occurs when goods must be delivered from a central depot to clients with known demands, usingk vehicles of fixed capacity. Each client must be assigned to exactly one of the vehicles. The set of clients assigned to each vehicle must satisfy the capacity constraint. The goal is to minimize the total distance traveled. When the capacity of the vehicles is large enough, this problem reduces to the famous traveling salesman problem (TSP). A variant of the problem in which each client is visited by at least one vehicle, called the graphical vehicle routing problem (GVRP), is also considered in this paper and used as a relaxation of CVRP. Our approach for CVRP and GVRP is to extend the polyhedral results known for TSP. For example, the subtour elimination constraints can be generalized to facets of both CVRP and GVRP. Interesting classes of facets arise as a generalization of the comb inequalities, depending on whether the depot is in a handle, a tooth, both or neither. We report on the optimal solution of two problem instances by a cutting plane algorithm that only uses inequalities from the above classes.
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    Mathematical programming 60 (1993), S. 81-92 
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    Keywords: Projective algorithm ; analytic centers ; dual ellipsoids
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    Notes: Abstract The primal projective algorithm for linear programs with unknown optimal objective function value is extended to the case where one uses a weighted Karmarkar potential function. This potential is defined with respect to a strict lower bound to the optimum. The minimization of this potential when the lower bound is kept fixed, yields a primal and a dual feasible solution. The dual solution is the weighted analytic center of a certain dual polytope. Finally one exhibits a pair of homothetic dual ellipsoids that extends results obtained by Sonnevend, Todd, Ye, Freund and Anstreicher.
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    Mathematical programming 60 (1993), S. 145-166 
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    Keywords: Network design ; LP relaxations ; worst-case analysis ; heuristics
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    Notes: Abstract We consider the survivable network design problem — the problem of designing, at minimum cost, a network with edge-connectivity requirements. As special cases, this problem encompasses the Steiner tree problem, the traveling salesman problem and thek-edge-connected network design problem. We establish a property, referred to as the parsimonious property, of the linear programming (LP) relaxation of a classical formulation for the problem. The parsimonious property has numerous consequences. For example, we derive various structural properties of these LP relaxations, we present some algorithmic improvements and we perform tight worst-case analyses of two heuristics for the survivable network design problem.
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    Mathematical programming 60 (1993), S. 167-185 
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    Keywords: Conjugate directions ; linear constraints ; matrix factorizations ; nonlinear optimization ; updating ; variable metric algorithms
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    Notes: Abstract Many iterative algorithms for optimization calculations use a second derivative approximation,B say, in order to calculate the search directiond = −B −1∇f(x). In order to avoid invertingB we work with matricesZ, whose columns satisfy the conjugacy relationsZ T BZ = I. We present an update ofZ that is compatible with members of the Broyden family that generate positive definite second derivative approximations. The algorithm requires only 3n 2+O(n) flops for the update ofZ and the calculation ofd. The columns of the resultantZ matrices have interesting conjugacy and orthogonality properties with respect to previous second derivative approximations and function gradients, respectively. The update also provides a simple proof of Dixon's theorem. For the BFGS method we adapt the algorithm in order to obtain a null space method for linearly constrained calculations.
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    Mathematical programming 50 (1991), S. 259-274 
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    Keywords: Concave minimization ; global optimization ; nonlinear programming ; nonconvex programming ; branch-and-bound ; outer approximation ; cutting planes
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    Notes: Abstract An algorithm is proposed for globally minimizing a concave function over a compact convex set. This algorithm combines typical branch-and-bound elements like partitioning, bounding and deletion with suitably introduced cuts in such a way that the computationally most expensive subroutines of previous methods are avoided. In each step, essentially only few linear programming problems have to be solved. Some preliminary computational results are reported.
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    Mathematical programming 50 (1991), S. 277-290 
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    Keywords: Algorithms ; complexity ; data structures ; dynamic trees ; graphs ; linear programming ; maximum flow ; network flow ; network optimization
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    Notes: Abstract Goldfarb and Hao (1990) have proposed a pivot rule for the primal network simplex algorithm that will solve a maximum flow problem on ann-vertex,m-arc network in at mostnm pivots and O(n 2 m) time. In this paper we describe how to extend the dynamic tree data structure of Sleator and Tarjan (1983, 1985) to reduce the running time of this algorithm to O(nm logn). This bound is less than a logarithmic factor larger than those of the fastest known algorithms for the problem. Our extension of dynamic trees is interesting in its own right and may well have additional applications.
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    Mathematical programming 50 (1991), S. 343-357 
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    Keywords: Primal—dual algorithm ; continuity of value functional ; asymptotic feasibility ; constraint-set-manipulation ; entropy maximization
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    Notes: Abstract A general primal—dual algorithm for linearly constrained optimization problems is formulated in which the dual variables are updated by a dual algorithmic operator. Convergence is proved under the assumption that the dual algorithmic operator implies asymptotic feasibility of the primal iterates with respect to the linear constraints. A general result relating the minimal values of an infinite sequence of constrained problems to the minimal value of a limiting problem (constrained by the limit of the sequence of constraints sets) is established and invoked. The applicability of the general theory is demonstrated by analyzing a specific dual algorithmic operator. This leads to the “MART” algorithm for constrained entropy maximization used in image reconstruction from projections.
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    Mathematical programming 50 (1991), S. 331-342 
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    Keywords: Potential reduction algorithm ; linear complementarity problem ; interior point algorithm ; Karmarkar's algorithm ; path of centers ; central trajectory
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    Notes: Abstract This paper proposes an interior point algorithm for a positive semi-definite linear complementarity problem: find an (x, y)∈ℝ 2n such thaty=Mx+q, (x,y)⩾0 andx T y=0. The algorithm reduces the potential function $$f(x,y) = (n + \sqrt n )\log x^T y - \sum\limits_{i = 1}^n {\log x_i y_i } $$ by at least 0.2 in each iteration requiring O(n 3) arithmetic operations. If it starts from an interior feasible solution with the potential function value bounded by $$O(\sqrt n L)$$ , it generates, in at most $$O(\sqrt n L)$$ iterations, an approximate solution with the potential function value $$ - O(\sqrt n L)$$ , from which we can compute an exact solution in O(n 3) arithmetic operations. The algorithm is closely related with the central path following algorithm recently given by the authors. We also suggest a unified model for both potential reduction and path following algorithms for positive semi-definite linear complementarity problems.
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    Mathematical programming 50 (1991), S. 359-366 
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    Keywords: Sharp minima ; proximal point ; finite termination
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    Notes: Abstract This paper concerns the notion of a sharp minimum on a set and its relationship to the proximal point algorithm. We give several equivalent definitions of the property and use the notion to prove finite termination of the proximal point algorithm.
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    Mathematical programming 50 (1991), S. 291-330 
    ISSN: 1436-4646
    Keywords: Karmarkar's algorithm ; modified Newton's method ; global Newton's method ; projective transformation ; logarithmic barrier function ; nonlinear optimization
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    Notes: Abstract This paper describes a full-dimensional version of Karmarkar's linear programming algorithm, theprojective scaling algorithm, which is defined for any linear program in ℝ n having a bounded, full-dimensional polytope of feasible solutions. If such a linear program hasm inequality constraints, then it is equivalent under an injective affine mappingJ:ℝ n →ℝ m to Karmarkar's original algorithm for a linear program in ℝ m havingm—n equality constraints andm inequality constraints. Karmarkar's original algorithm minimizes a potential functiong(x), and the projective scaling algorithm is equivalent to that version of Karmarkar's algorithm whose step size minimizes the potential function in the step direction. The projective scaling algorithm is shown to be a global Newton method for minimizing a logarithmic barrier function in a suitable coordinate system. The new coordinate system is obtained from the original coordinate system by a fixed projective transformationy = Ф(x) which maps the hyperplaneH opt ={x:〈c, x〉 =c 0} specified by the optimal value of the objective function to the “hyperplane at infinity”. The feasible solution set is mapped underФ to anunbounded polytope. Letf LB(y) denote the logarithmic barrier function associated to them inequality constraints in the new coordinate system. It coincides up to an additive constant with Karmarkar's potential function in the new coordinate system. Theglobal Newton method iterate y * for a strictly convex functionf(y) defined on a suitable convex domain is that pointy * that minimizesf(y) on the search ray {y+λ v N(y): λ≧0} wherev N(y) =−(∇2 f(y))−1(∇f(y)) is the Newton's method vector. If {x (k)} are a set of projective scaling algorithm iterates in the original coordinate system andy (k) =Ф(x (k)) then {y (k)} are a set of global Newton method iterates forf LB(y) and conversely. Karmarkar's algorithm with step size chosen to minimize the potential function is known to converge at least at a linear rate. It is shown (by example) that this algorithm does not have a superlinear convergence rate.
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    Mathematical programming 61 (1993), S. 357-375 
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    Keywords: Multiobjective linear programming ; efficient set ; degeneracy ; simplex algorithm
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    Notes: Abstract An algorithm for finding the whole efficient set of a multiobjective linear program is proposed. From the set of efficient edges incident to a vertex, a characterization of maximal efficient faces containing the vertex is given. By means of the lexicographic selection rule of Dantzig, Orden and Wolfe, a connectedness property of the set of dual optimal bases associated to a degenerate vertex is proved. An application of this to the problem of enumerating all the efficient edges incident to a degenerate vertex is proposed. Our method is illustrated with numerical examples and comparisons with Armand—Malivert's algorithm show that this new algorithm uses less computer time.
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    Mathematical programming 62 (1993), S. 1-14 
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    Keywords: Greedy algorithm ; Monge arrays ; series parallel graphs ; linear programming ; network flow ; transportation problem ; integrality ; convexity
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    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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    Mathematical programming 62 (1993), S. 133-151 
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    Keywords: Clustering ; decomposition ; column generation ; subproblem optimization ; valid inequality ; compiler design
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    Notes: Abstract We describe a decomposition framework and a column generation scheme for solving a min-cut clustering problem. The subproblem to generate additional columns is itself an NP-hard mixed integer programming problem. We discuss strong valid inequalities for the subproblem and describe some efficient solution strategies. Computational results on compiler construction problems are reported.
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    Mathematical programming 61 (1993), S. 351-356 
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    Keywords: Classes of matrices ; linear complementarity problem
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    Notes: Abstract We consider the linear complementarity problem (LCP),w=Az + q, w⩾0,z⩾0,w T z=0, when all the off-diagonal entries ofA are nonpositive (the class of Z-matrices), all the proper principal minors ofA are positive and the determinant ofA is negative (the class of almost P-matrices). We shall call this the class of F-matrices. We show that ifA is a Z-matrix, thenA is an F-matrix if and only if LCP(q, A) has exactly two solutions for anyq⩾0,q≠0, and has at most two solutions for any otherq.
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    Mathematical programming 61 (1993), S. 377-384 
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    Keywords: Perturbed convex programs ; interval analysis ; sensitivity analysis ; nonlinear programming
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    Notes: Abstract Several authors have used interval arithmetic to deal with parametric or sensitivity analysis in mathematical programming problems. Several reported computational experiments have shown how interval arithmetic can provide such results. However, there has not been a characterization of the resulting solution interval in terms of the usual sensitivity analysis results. This paper presents a characterization of perturbed convex programs and the resulting solution intervals. Interval arithmetic was developed as a mechanism for dealing with the inherent error associated with numerical computations using a computational device. Here it is used to describe error in the parameters. We show that, for convex programs, the resulting solution intervals can be characterized in terms of the usual sensitivity analysis results. It has been often reported in the literature that even well behaved convex problems can exhibit pathological behavior in the presence of data perturbations. This paper uses interval arithmetic to deal with such problems, and to characterize the behavior of the perturbed problem in the resulting interval. These results form the foundation for future computational studies using interval arithmetic to do nonlinear parametric analysis.
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    Mathematical programming 61 (1993), S. 385-397 
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    Keywords: 90C25 ; 90C29 ; Constraint qualification ; convex program ; optimality condition ; multi-objective program
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    Notes: Abstract We introduce and characterize a class of differentiable convex functions for which the Karush—Kuhn—Tucker condition is necessary for optimality. If some constraints do not belong to this class, then the characterization of optimality generally assumes an asymptotic form. We also show that for the functions that belong to this class in multi-objective optimization, Pareto solutions coincide with strong Pareto solutions,. This extends a result, well known for the linear case.
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    Notes: Abstract The problem of integer programming in bounded variables, over constraints with no more than two variables in each constraint is NP-complete, even when all variables are binary. This paper deals with integer linear minimization problems inn variables subject tom linear constraints with at most two variables per inequality, and with all variables bounded between 0 andU. For such systems, a 2-approximation algorithm is presented that runs in time O(mnU 2 log(Un 2 m)), so it is polynomial in the input size if the upper boundU is polynomially bounded. The algorithm works by finding first a super-optimal feasible solution that consists of integer multiples of 1/2. That solution gives a tight bound on the value of the minimum. It furthermore has an identifiable subset of integer components that retain their value in an integer optimal solution of the problem. These properties are a generalization of the properties of the vertex cover problem. The algorithm described is, in particular, a 2-approximation algorithm for the problem of minimizing the total weight of true variables, among all truth assignments to the 2-satisfiability problem.
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    Mathematical programming 62 (1993), S. 85-93 
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    Keywords: Bigℳ ; affine scaling algorithm ; linear program ; interior point algorithm ; infeasibility ; global convergence
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    Notes: Abstract When we apply the affine scaling algorithm to a linear program, we usually construct an artificial linear program having an interior feasible solution from which the algorithm starts. The artificial linear program involves a positive number called the bigℳ. Theoretically, there exists anℳ * such that the original problem to be solved is equivalent to the artificial linear program ifℳ 〉ℳ *. Practically, however, such anℳ * is unknown and a safe estimate ofℳ is often too large. This paper proposes a method of updatingℳ to a suitable value during the iteration of the affine scaling algorithm. Asℳ becomes large, the method gives information on infeasibility of the original problem or its dual.
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    Mathematical programming 62 (1993), S. 199-213 
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    Keywords: Transportation problem ; assignment problem ; supply ; demand ; staircase ; capacity ; lattice ; sublattice ; superadditive ; subadditive ; greatest element ; myopic ; greedy ; integral ; linear time ; complexity ; bivariate distribution ; correlation ; distribution ; substitution ; inventory ; age ; FIFO ; LIFO
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    Notes: Abstract A cumulative-capacitated transportation problem is studied. The supply nodes and demand nodes are each chains. Shipments from a supply node to a demand node are possible only if the pair lies in a sublattice, or equivalently, in a staircase disjoint union of rectangles, of the product of the two chains. There are (lattice) superadditive upper bounds on the cumulative flows in all leading subrectangles of each rectangle. It is shown that there is a greatest cumulative flow formed by the natural generalization of the South-West Corner Rule that respects cumulative-flow capacities; it has maximum reward when the rewards are (lattice) superadditive; it is integer if the supplies, demands and capacities are integer; and it can be calculated myopically in linear time. The result is specialized to earlier work of Hoeffding (1940), Fréchet (1951), Lorentz (1953), Hoffman (1963) and Barnes and Hoffman (1985). Applications are given to extreme constrained bivariate distributions, optimal distribution with limited one-way product substitution and, generalizing results of Derman and Klein (1958), optimal sales with age-dependent rewards and capacities.
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    Mathematical programming 62 (1993), S. 321-357 
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    Keywords: Nonsmooth optimization ; convex composite optimization ; generalized gradient ; maximum eigenvalue ; sum of eigenvalues
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    Notes: Abstract The sum of the largest eigenvalues of a symmetric matrix is a nonsmooth convex function of the matrix elements. Max characterizations for this sum are established, giving a concise characterization of the subdifferential in terms of a dual matrix. This leads to a very useful characterization of the generalized gradient of the following convex composite function: the sum of the largest eigenvalues of a smooth symmetric matrix-valued function of a set of real parameters. The dual matrix provides the information required to either verify first-order optimality conditions at a point or to generate a descent direction for the eigenvalue sum from that point, splitting a multiple eigenvalue if necessary. Connections with the classical literature on sums of eigenvalues and eigenvalue perturbation theory are discussed. Sums of the largest eigenvalues in the absolute value sense are also addressed.
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    Mathematical programming 62 (1993), S. 415-425 
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    Keywords: 90C20 ; 90C30 ; 90C31 ; 90C33 ; Normal maps ; nonsingularity ; sensitivity analysis ; nonlinear programming ; quadratic programming ; complementarity
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    Notes: Abstract Normal maps are single-valued, generally nonsmooth functions expressing conditions for the solution of variational problems such as those of optimization or equilibrium. Normal maps arising from linear transformations are particularly important, both in their own right and as predictors of the behavior of related nonlinear normal maps. They are called (locally or globally)nonsingular if the functions appearing in them are (local or global) homeomorphisms satisfying a Lipschitz condition. We show here that when the linear transformation giving rise to such a normal map has a certain symmetry property, the necessary and sufficient condition for nonsingularity takes a particularly simple and convenient form, being simply a positive definiteness condition on a certain subspace.
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    Mathematical programming 62 (1993), S. 475-496 
    ISSN: 1436-4646
    Keywords: Center location ; tree networks ; least element property
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    Notes: Abstract In the classicalp-center location model on a network there is a set of customers, and the primary objective is to selectp service centers that will minimize the maximum distance of a customer to a closest center. Suppose that thep centers receive their supplies from an existing central depot on the network, e.g. a warehouse. Thus, a secondary objective is to locate the centers that optimize the primary objective “as close as possible” to the central depot. We consider tree networks and twop-center models. We show that the set of optimal solutions to the primary objective has a semilattice structure with respect to some natural ordering. Using this property we prove that there is ap-center solution to the primary objective that simultaneously minimizes every secondary objective function which is monotone nondecreasing in the distances of thep centers from the existing central depot. Restricting the location models to a rooted path network (real line) we prove that the above results hold for the respective classicalp-median problems as well.
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    Mathematical programming 62 (1993), S. 553-556 
    ISSN: 1436-4646
    Keywords: Pivoting ; normalize ; basic matrix
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    Notes: Abstract Given a matrix in basic form a sequence of pivots is described which brings the matrix into a basic form where the maximum absolute component is unity.
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    Mathematical programming 62 (1993), S. 575-580 
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    Keywords: Non-convex non-linear programming ; global optimization ; second-order optimality conditions ; copositive matrices ; quadratic programming ; convex maximization problem
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    Notes: Abstract In this note we specify a necessary and sufficient condition for global optimality in concave quadratic minimization problems. Using this condition, it follows that, from the perspective of worst-case complexity of concave quadratic problems, the difference between local and global optimality conditions is not as large as in general. As an essential ingredient, we here use theε-subdifferential calculus via an approach of Hiriart-Urruty and Lemarechal (1990).
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    Mathematical programming 63 (1994), S. 109-125 
    ISSN: 1436-4646
    Keywords: Alternative theorems ; quasidifferentiable programming ; nonsmooth analysis ; optimality conditions ; difference convex programming
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    Notes: Abstract A new generalized Farkas theorem of the alternative is presented for systems involving functions which can be expressed as the difference of sublinear functions. Various other forms of theorems of the alternative are also given using quasidifferential calculus. Comprehensive optimality conditions are then developed for broad classes of infinite dimensional quasidifferentiable programming problems. Applications to difference convex programming and infinitely constrained concave minimization problems are also discussed.
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    Mathematical programming 63 (1994), S. 83-108 
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    Keywords: Convex programming ; deep cut ellipsoid algorithm ; rate of convergence ; location theory ; min—max programming
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    Notes: Abstract This paper proposes a deep cut version of the ellipsoid algorithm for solving a general class of continuous convex programming problems. In each step the algorithm does not require more computational effort to construct these deep cuts than its corresponding central cut version. Rules that prevent some of the numerical instabilities and theoretical drawbacks usually associated with the algorithm are also provided. Moreover, for a large class of convex programs a simple proof of its rate of convergence is given and the relation with previously known results is discussed. Finally some computational results of the deep and central cut version of the algorithm applied to a min—max stochastic queue location problem are reported.
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    Mathematical programming 63 (1994), S. 157-182 
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    Keywords: Steiner tree ; series—parallel graphs ; polyhedral characterization ; projection ; facets ; formulations
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    Notes: Abstract We consider the vertex-weighted version of the undirected Steiner tree problem. In this problem, a cost is incurred both for the vertices and the edges present in the Steiner tree. We completely describe the associated polytope by linear inequalities when the underlying graph is series—parallel. For general graphs, this formulation can be interpreted as a (partial) extended formulation for the Steiner tree problem. By projecting this formulation, we obtain some very large classes of facet-defining valid inequalities for the Steiner tree polytope.
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    Mathematical programming 63 (1994), S. 213-234 
    ISSN: 1436-4646
    Keywords: Max-flow problem ; min-cut problem ; duality gap
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    Notes: Abstract Strang (Mathematical Programming 26, 1983) gave a method to establish a max-flow min-cut theorem in a domain of a Euclidean space. The method can be applied also to max-flow min-cut problems defined by Iri (Survey of Mathematical Programming, North-Holland, 1979) whenever the capacity functions of max-flow problems are bounded and continuous. This paper deals with max-flow min-cut problems of Strang and Iri with unbounded or noncontinuous capacity functions. It is proved that, in such problems, max-flow min-cut theorems may fail to hold.
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    Mathematical programming 51 (1991), S. 281-305 
    ISSN: 1436-4646
    Keywords: Sequential quadratic programming ; parameter identification ; 65K10 ; 65H10 ; 49D37
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    Notes: Abstract We analyze the method of sequential quadratic programming for equality constrained minimization problems in Hilbert spaces of functions, and for the discrete approximations of such problems in the context of an elliptic parameter identification problem. We show how the discretization can be constructed so as to preserve the convergence behavior of the iterates for the infinite dimensional problem in the finite dimensional approximations. We use the structure of the parameter identification problem to reduce the size of the linear system for the SQP step and verify nondegeneracy of the constraints.
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    Mathematical programming 51 (1991), S. 307-331 
    ISSN: 1436-4646
    Keywords: Convex programming ; epi-convergence ; Mosco-convergence ; penalization ; perturbation ; proximal regularization ; variational convergence
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    Notes: Abstract A penalty method for convex functions which cannot necessarily be extended outside their effective domains by an everywhere finite convex function is proposed and combined with the proximal method. Proofs of convergence rely on variational convergence theory.
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    Mathematical programming 51 (1991), S. 349-358 
    ISSN: 1436-4646
    Keywords: 60F05 ; 60G50 ; 90C10 ; Multiknapsack value function ; Lagrangean relaxation ; asymptotic characterization ; uniform strong law of large numbers ; uniform law of the iterated logarithm ; Vapnik—Chervonenkis class ; asymptotic normality
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    Notes: Abstract In Meanti et al. (1990) an almost sure asymptotic characterization has been derived for the optimal solution value as function of the knapsack capacities, when the profit and requirement coefficients of items to be selected from are random variables. In this paper we establish a rate of convergence for this process using results from the theory of empirical processes.
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    Mathematical programming 51 (1991), S. 333-348 
    ISSN: 1436-4646
    Keywords: Quasidifferential calculus ; nonlinear nonsmooth optimization ; stability theory ; decomposition
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    Notes: Abstract This paper is concerned with the optimal value function arising in the primal decomposition of a quasidifferentiable programming problem. In particular, estimates for the upper Dini directional derivative of this function are derived. They involve certain “Lagrange multipliers” occurring in the necessary minimum conditions to the lower level problems. This study generalizes some previously published results on this subject.
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    Mathematical programming 51 (1991), S. 359-400 
    ISSN: 1436-4646
    Keywords: Traveling salesman ; polytope ; facet ; composition of facets ; inequality ; graph ; Hamilton cycle ; tour
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    Notes: Abstract The graphical relaxation of the Traveling Salesman Problem is the relaxation obtained by requiring that the salesman visit each city at least once instead of exactly once. This relaxation has already led to a better understanding of the Traveling Salesman polytope in Cornuéjols, Fonlupt and Naddef (1985). We show here how one can compose facet-inducing inequalities for the graphical traveling salesman polyhedron, and obtain other facet-inducing inequalities. This leads to new valid inequalities for the Symmetric Traveling Salesman polytope. This paper is the first of a series of three papers on the Symmetric Traveling Salesman polytope, the next one studies the strong relationship between that polytope and its graphical relaxation, and the last one applies all the theoretical developments of the two first papers to prove some new facet-inducing results.
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    Mathematical programming 51 (1991), S. 401-419 
    ISSN: 1436-4646
    Keywords: Steiner trees ; arborescences ; polyhedral theory ; facets ; lifting and composition of facets
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    Notes: Abstract The Steiner arborescence (or Steiner directed tree) problem concerns the connection of a set of target vertices of a digraph to a given root vertex. This problem is known to be NP-hard. In the present paper we study the facial structure of two polyhedra associated with the problem. Several classes of valid inequalities are considered, and a new class with arbitrarily large coefficients is introduced. All these inequalities are shown to define distinct facets of both the Steiner polyhedra considered. This is achieved by exploiting two lifting theorems which also allow generalization of the new inequalities. Composition theorems are finally given and used to derive large families of new facet-inducing inequalities with exponentially large coefficients.
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    Mathematical programming 51 (1991), S. 421-421 
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    Mathematical programming 52 (1991), S. 59-69 
    ISSN: 1436-4646
    Keywords: Integer programming ; branch and bound ; pseudo-shadow-prices
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    Notes: Abstract During a branch and bound search of an integer programme, variables may be declared fixed, and rows may be declared redundant. The purpose of this paper is to report some results from forcing such fixed variables out of the basis in every node of a branch and bound search. At the same time non-basic slacks at zero activity on redundant rows are forced into the basis. Both changes consume computer time, but result in a less dense basis and in some cases in better branching.
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    Mathematical programming 52 (1991), S. 11-17 
    ISSN: 1436-4646
    Keywords: Modeling ; cancer ; optimization ; optimal control ; drug delivery
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    Notes: Abstract In this paper we consider the problems of modeling the tumor growth and optimize the chemotherapy treatment. A biologically based model is used with the goal of solving an optimization problem involving discrete delivery of antineoplastic drugs. Our model is formulated via compartmental analysis in order to take into account the cell cycle. The cost functional measures not only the final size of the tumor but also the total amount of drug delivered. We propose an algorithm based on the discrete maximum principle to solve the optimal drug schedule problem. Our numerical results show nice interpretations from the medical point of view.
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    Mathematical programming 52 (1991), S. 19-27 
    ISSN: 1436-4646
    Keywords: Nonlinear optimization ; sequential quadratic programming ; quadratic programming ; line search
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    Notes: Abstract We produced a nonlinear optimization software program which is based on a Sequential Quadratic Programming (SQP) method (Schittkowski, 1981a). Our program has several original characteristics: (1) automatic choice between two QP solvers, the Goldfarb—Idnani (GI) method (Goldfarb and Idnani, 1983) and the Least Squares (LS) method (Schittkowski, 1981b); (2) an augmented Lagrange function (Schittkowski, 1981a) as the objective function for line search; (3) adaptive Armijo method for line search; (4) direct definition of upper and lower bounds for variables and constraint functions; and (5) accurate numerical differentials. These characteristics ensure the reliability of our program for solving standard problems. In this paper, point (3) is described in detail. Then, the program is applied to an actual problem, the optimal placement of blocks. A model for this problem has been suggested by Sha and Dutton (1984), but it was unsuited to treatment by the SQP method. Thus we modify it to ensure program applicability.
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    Notes: Abstract The uneven distribution of ventilation—perfusion ratios ( $$\dot V_A /\dot Q$$ ) in diseased lungs is the major cause of arterial hypoxemia. Farhi and Yokoyama (1967) and Yokoyama and Farhi (1967) were the first who used physiologically inert gases as indicator gases to assess the uneven distribution of $$\dot V_A /\dot Q$$ Wagner and his coworkers in San Diego (1977b) extended the method and elaborated the multiple inert gas elimination technique in which blood flows in 50 compartments with different $$\dot V_A /\dot Q$$ were estimated based on data for 6 indicator gases. They analyzed the indicator gas data through an enforced smoothing technique with the ridge regression. To get smooth distributions, they introduced a weighting function for $$\dot V_A /\dot Q$$ compartments and an additional treatment for the non-negativity of the blood flow. The weighting function was empirically obtained. We analyzed the data without putting any weights on $$\dot V_A /\dot Q$$ compartments nor any additional treatment for non-negativity of blood flow. The analytical method in the present study was a modified Newton method, which is one of the enforced smoothing method. Our method was capable of recovering all distribution patterns that were found through the method reported by Wagner et al. (1977b).
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    Mathematical programming 52 (1991), S. 45-58 
    ISSN: 1436-4646
    Keywords: Gas and water distribution networks ; sequential linear programming ; trust regions ; sparse matrix techniques
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    Notes: Abstract The paper treats a piping system, where the layout of the network is given but the diameters of the pipes should be chosen among a small number of different values. The cost of realizing the system should be minimized while keeping the energy heads at the nodes above some lower limits. A new algorithm using successive linear programming is presented. The performance of the algorithm is illustrated by optimizing a network with 201 pipes and 172 nodes. It is concluded that the new algorithm seems to be very efficient and stable, and that it always finds a solution with a cost near the best possible.
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    Mathematical programming 52 (1991), S. 99-123 
    ISSN: 1436-4646
    Keywords: Location problem ; average distance
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    Notes: Abstract This study is concerned with the problem of measuring average distances between two points in two different coplanar regions. The objectives are: (1) to derive the approximated average distances associated with circular regions and to check their accuracy; and (2) to apply these approximated distances to location problems. Results show that the simple approximate formulas are accurate and useful. The approximated average distances can be applied to the analyses of varied kinds of movement phenomena in cities.
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    Keywords: Multiobjective approach ; group decision making ; regional utility function ; game theory ; nucleolus
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    Notes: Abstract This paper concerns a methodological reflection on the multiobjective approach to public systems which involve group decision processes. Particular attention is given to an integrated program of regional systems which include value trade-offs between multiple objectives. Our intention is to combine the judgmental processes with the optimization processes in the “soft” public systems. A two-layer approach is applied. At the first layer, each regional program is formulated in mathematical programming based on a utility assessment with different regional characteristics. Each subsystem independently reflects its particular concern as a single agent. The dual optimal solutions obtained for each subsystem are treated as an index, or the theoretical prices, representing the value trade-offs among the multiple objectives. At the second layer, an effective formation of interregional cooperation for compromising the conflicting regional interests is examined. Ann-person cooperative game in the characteristic function form is used to evaluate the effectiveness of the cooperation. The characteristic function for the game is derived on the incremental value of the regional benefit after the formation of a cooperation. The nucleolus and the augmented nucleolus as the solution concepts of the cooperative game are used for indicating the effectiveness of the cooperation. Finally using alternative criteria, the results in assessing the best decisions are examined comparatively.
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    Mathematical programming 52 (1991), S. 29-43 
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    Keywords: VLSI-CAD ; floor-plan ; rectangular dual ; drawing of a rectangular dual ; lower bound estimation
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    Notes: Abstract In this paper, we consider a problem to seek a rectangular dual $$\tilde D$$ and its area-efficient drawing such that $$\tilde D$$ can be drawn in the smallest area among all rectangular duals under the constraints imposed not only on the area and the minimum dimension of each face but also on the length of abutment between two adjacent faces. Since the problem is hard to solve, we tackle this problem in an exhaustive manner by using an algorithm to enumerate all the rectangular duals. In order to make this exhaustive method efficient, we propose the following two algorithms working under the constraints stated above; an algorithm to find an area-efficient drawing of a given rectangular dual, and an algorithm to estimate a lower bound to the area required to draw a given rectangular dual. We also show some esperimental results to demonstrate the effectiveness of the lower bound. The area-efficient drawing of $$\tilde D$$ can be used as a VLSI floor-plan by regarding each inner face of $$\tilde D$$ as an area for a block to be placed.
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    Mathematical programming 66 (1994), S. 273-281 
    ISSN: 1436-4646
    Keywords: Minimal test sets for integer programming ; Simplicial complexes ; Maximal lattice free bodies
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    Notes: Abstract The simplicial complexK(A) is defined to be the collection of simplices, and their proper subsimplices, representing maximal lattice free bodies of the form (x: Ax⩽b), withA a fixed generic (n + 1) ×n matrix. The topological space associated withK(A) is shown to be homeomorphic to ℝ n , and the space obtained by identifying lattice translates of these simplices is homeorphic to then-torus.
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    Mathematical programming 66 (1994), S. 313-325 
    ISSN: 1436-4646
    Keywords: Network ; Multicommodity flow ; Minimum cost flow ; Edge-disjoint paths
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    Notes: Abstract LetN = (G, T, c, a) be a network, whereG is an undirected graph,T is a distinguished subset of its vertices (calledterminals), and each edgee ofG has nonnegative integer-valuedcapacity c(e) andcost a(e). Theminimum cost maximum multi(commodity)flow problem (*) studied in this paper is to find ac-admissible multiflowf inG such that: (i)f is allowed to contain partial flows connecting any pairs of terminals, (ii) the total value off is as large as possible, and (iii) the total cost off is as small as possible, subject to (ii). This generalizes, on one hand, the undirected version of the classical minimum cost maximum flow problem (when |T| = 2), and, on the other hand, the problem of finding a maximum fractional packing ofT-paths (whena ≡ 0). Lovász and Cherkassky independently proved that the latter has a half-integral optimal solution. A pseudo-polynomial algorithm for solving (*) has been developed earlier and, as its consequence, the theorem on the existence of a half-integral optimal solution for (*) was obtained. In the present paper we give a direct, shorter, proof of this theorem. Then we prove the existence of a half-integral optimal solution for the dual problem. Finally, we show that half-integral optimal primal and dual solutions can be designed by a combinatorial strongly polynomial algorithm, provided that some optimal dual solution is known (the latter can be found, in strongly polynomial time, by use of a version of the ellipsoid method).
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    Mathematical programming 66 (1994), S. 379-402 
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    Keywords: Primary 49A52, 90C30 ; Secondary 26E15, 58C20 ; Nonsmooth analysis ; Second order necessary and sufficient conditions
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    Notes: Abstract In this paper we generalize and sharpen R.W. Chaney's results on unconstrained and constrained second-order necessary and sufficient optimality conditions [5–7] for general Lipschitz functions without the semismoothness assumption
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    Mathematical programming 67 (1994), S. 1-28 
    ISSN: 1436-4646
    Keywords: Error bound ; Analytic systems ; Karush—Kuhn—Tucker conditions ; Affine variational inequality ; Complementarity problem ; Integer feasibility problem
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    Notes: Abstract Using a 1958 result of Lojasiewicz, we establish an error bound for analytic systems consisting of equalities and inequalities defined by real analytic functions. In particular, we show that over any bounded region, the distance from any vectorx in the region to the solution set of an analytic system is bounded by a residual function, raised to a certain power, evaluated atx. For quadratic systems satisfying certain nonnegativity assumptions, we show that this exponent is equal to 1/2. We apply the error bounds to the Karush—Kuhn—Tucker system of a variational inequality, the affine variational inequality, the linear and nonlinear complementarity problem, and the 0–1 integer feasibility problem, and obtain new error bound results for these problems. The latter results extend previous work for polynomial systems and explain why a certain square-root term is needed in an error bound for the (monotone) linear complementarity problem.
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    Mathematical programming 67 (1994), S. 29-51 
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    Keywords: Infeasible-interior-point methods ; Linear complementarity problems ; Q-subquadratic convergence
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    Notes: Abstract We modify the algorithm of Zhang to obtain anO(n2L) infeasible-interior-point algorithm for monotone linear complementarity problems that has an asymptoticQ-subquadratic convergence rate. The algorithm requires the solution of at most two linear systems with the same coefficient matrix at each iteration.
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    Mathematical programming 67 (1994), S. 99-108 
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    Keywords: Stochastic programming with recourse ; Quantitative stability ; Lipschitz continuity ; Law of Iterated Logarithm ; Kolmogorov—Smirnov distance
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    Notes: Abstract In this paper we study stability of optimal solutions of stochastic programming problems with fixed recourse. An upper bound for the rate of convergence is given in terms of the objective functions of the associated deterministic problems. As an example it is shown how it can be applied to derivation of the Law of Iterated Logarithm for the optimal solutions. It is also shown that in the case of simple recourse this upper bound implies upper Lipschitz continuity of the optimal solutions with respect to the Kolmogorov—Smirnov distance between the corresponding cumulative probability distribution functions.
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    Mathematical programming 67 (1994), S. 109-119 
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    Keywords: Polynomial-time ; Linear programming ; Primal-dual ; Interior-point algorithm
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    Notes: Abstract Kojima, Megiddo, and Mizuno investigate an infeasible-interior-point algorithm for solving a primal—dual pair of linear programming problems and they demonstrate its global convergence. Their algorithm finds approximate optimal solutions of the pair if both problems have interior points, and they detect infeasibility when the sequence of iterates diverges. Zhang proves polynomial-time convergence of an infeasible-interior-point algorithm under the assumption that both primal and dual problems have feasible points. In this paper, we show that a modification of the Kojima—Megiddo—Mizuno algorithm “solves” the pair of problems in polynomial time without assuming the existence of the LP solution. Furthermore, we develop anO(nL)-iteration complexity result for a variant of the algorithm.
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    Mathematical programming 52 (1991), S. 285-302 
    ISSN: 1436-4646
    Keywords: Nondifferentiable minimization ; convex programming ; exact penalty functions ; numerical methods ; descent methods ; proximal bundle methods ; Primary 65K05 ; Secondary 90C25
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    Notes: Abstract This paper presents new versions of proximal bundle methods for solving convex constrained nondifferentiable minimization problems. The methods employϑ 1 orϑ ∞ exact penalty functions with new penalty updates that limit unnecessary penalty growth. In contrast to other methods, some of them are insensitive to problem function scaling. Global convergence of the methods is established, as well as finite termination for polyhedral problems. Some encouraging numerical experience is reported. The ideas presented may also be used in variable metric methods for smooth nonlinear programming.
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    Mathematical programming 67 (1994), S. 325-341 
    ISSN: 1436-4646
    Keywords: Minimum capacity cut ; Network flow ; Polynomial algorithm
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    Notes: Abstract In this paper, we present an efficient implementation of theO(mn + n 2 logn) time algorithm originally proposed by Nagamochi and Ibaraki (1992) for computing the minimum capacity cut of an undirected network. To enhance computation, various ideas are added so that it can contract as many edges as possible in each iteration. To evaluate the performance of the resulting implementation, we conducted extensive computational experiments, and compared the results with that of Padberg and Rinaldi's algorithm (1990), which is currently known as one of the practically fastest programs for this problem. The results indicate that our program is considerably faster than Padberg and Rinaldi's program, and its running time is not significantly affected by the types of the networks being solved.
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    Mathematical programming 52 (1991), S. 303-313 
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    Keywords: Integer nonlinear programming ; Lagrangean decomposition ; Lagrangean relaxation
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    Notes: Abstract We present a Lagrangean decomposition to study integer nonlinear programming problems. Solving the dual Lagrangean relaxation we have to obtain at each iteration the solution of a nonlinear programming with continuous variables and an integer linear programming. Decreasing iteratively the primal—dual gap we propose two algorithms to treat the integer nonlinear programming.
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    Mathematical programming 52 (1991), S. 315-357 
    ISSN: 1436-4646
    Keywords: Travelling salesman problem ; problem formulations ; convex polyhedral cones ; polyhedra ; affine transformations
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    Notes: Abstract A transformation technique is proposed that permits one to derive the linear description of the imageX of a polyhedronZ under an affine linear transformation from the (given) linear description ofZ. This result is used to analytically compare various formulations of the asymmetric travelling salesman problem to the standard formulation due to Dantzig, Fulkerson and Johnson which are all shown to be “weaker formulations” in a precise setting. We also apply this transformation technique to “symmetrize” formulations and show, in particular, that the symmetrization of the standard asymmetric formulation results into the standard one for the symmetric version of the travelling salesman problem.
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    Mathematical programming 52 (1991), S. 405-414 
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    Keywords: Linear programming ; primal and dual ; potential reduction algorithm ; affine scaling algorithm
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    Notes: Abstract We analyze several affine potential reduction algorithms for linear programming based on simplifying assumptions. We show that, under a strong probabilistic assumption regarding the distribution of the data in an iteration, the decrease in the primal potential function will be $$\Omega (\rho /\sqrt {log(n)} )$$ with high probability, compared to the guaranteedΩ(1). (ρ ⩾2n is a parameter in the potential function andn is the number of variables.) Under the same assumption, we further show that the objective reduction rate of Dikin's affine scaling algorithm is $$(1 - 1/\sqrt {log(n)} )$$ with high probability, compared to no guaranteed convergence rate.
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    Mathematical programming 52 (1991), S. 377-404 
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    Keywords: Linear programming ; interior point methods ; affine scaling methods ; global analysis ; degenerate problems
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    Notes: Abstract In this paper we show the global convergence of the affine scaling methods without assuming any condition on degeneracy. The behavior of the method near degenerate faces is analyzed in detail on the basis of the equivalence between the affine scaling methods for homogeneous LP problems and Karmarkar's method. It is shown that the step-size 1/8, where the displacement vector is normalized with respect to the distance in the scaled space, is sufficient to guarantee the global convergence of the affine scaling methods.
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    Mathematical programming 52 (1991), S. 415-428 
    ISSN: 1436-4646
    Keywords: Interior point methods ; linear programming ; potential reduction methods ; Karmarkar's algorithm
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    Notes: Abstract We present a procedure for computing lower bounds for the optimal cost in a linear programming problem. Whenever the procedure succeeds, it finds a dual feasible slack and the associated duality gap. Although no projective transformations or problem restatements are used, the method coincides with the procedures by Todd and Burrell and by de Ghellinck and Vial when these procedures are applicable. The procedure applies directly to affine potential reduction algorithms, and improves on existent techniques for finding lower bounds.
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    Mathematical programming 52 (1991), S. 429-439 
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    Keywords: Linear programming ; potential function ; phase I ; phase II ; artificial variable
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    Notes: Abstract We develop an extension of the affinely scaled potential reduction algorithm which simultaneously obtains feasibility and optimality in a standard form linear program, without the addition of any “M” terms. The method, and its lower-bounding procedure, are particularly simple compared with previous interior algorithms not requiring feasibility.
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    Mathematical programming 52 (1991), S. 527-553 
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    Topics: Computer Science , Mathematics
    Notes: Abstract A class of algorithms is proposed for solving linear programming problems (withm inequality constraints) by following the central path using linear extrapolation with a special adaptive choice of steplengths. The latter is based on explicit results concerning the convergence behaviour of Newton's method to compute points on the central pathx(r), r〉0, and this allows to estimate the complexity, i.e. the total numberN = N(R, δ) of steps needed to go from an initial pointx(R) to a final pointx(δ), R〉δ〉0, by an integral of the local “weighted curvature” of the (primal—dual) path. Here, the central curve is parametrized with the logarithmic penalty parameterr↓0. It is shown that for large classes of problems the complexity integral, i.e. the number of stepsN, is not greater than constm α log(R/δ), whereα 〈 1/2 e.g.α = 1/4 orα = 3/8 (note thatα = 1/2 gives the complexity of zero order methods). We also provide a lower bound for the complexity showing that for some problems the above estimation can hold only forα ⩾ 1/3. As a byproduct, many analytical and structural properties of the primal—dual central path are obtained: there are, for instance, close relations between the weighted curvature and the logarithmic derivatives of the slack variables; the dependence of these quantities on the parameterr is described. Also, related results hold for a family of weighted trajectories, into which the central path can be embedded.
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  • 96
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    Mathematical programming 52 (1991), S. 555-586 
    ISSN: 1436-4646
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper gives computational results for an efficient implementation of a variant of dual projective algorithm for linear programming. The implementation uses the preconditioned conjugate gradient method for computing projections. Our computational experience reported in this paper indicates that this algorithm has potential as an alternative for solving very large LPs in which the direct methods fail due to memory and CPU time requirements. The conjugate gradient algorithm was able to find very accurate directions even when the system was ill-conditioned. The paper also discusses a new mathematical technique called the reciprocal estimates for estimating the primal variables. We have conducted extensive computational experiments on problems representative of large classes of applications of current interest. We have also chosen instances of the problems of future potential interest, which could not be solved in the past due to the weakness of the prior solution methods, but which represent a large class of new applications. The hypergraph model is such an example. Comparison of our implementation with MINOS 5.1 shows that our implementation is orders of magnitude faster than MINOS 5.1 for these problems.
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  • 97
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    Mathematical programming 52 (1991), S. 587-595 
    ISSN: 1436-4646
    Keywords: Linear programming ; linear complementarity problem ; interior point algorithms ; path following algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper we propose an O(n 3 L) algorithm which is a modification of the path following algorithm [8] for a linear complementarity problem. The path following algorithm has to take a short step size in each iteration in order to bound the number of overall arithmetic operations by O(n 3 L). In practical computation, we can determine the step size adaptively. Mizuno, Yoshise, and Kikuchi [11] reported that such an adaptive algorithm required about O(L) iterations for some test problems. Here we show that we can use a rank one update technique in the adaptive algorithm so that the number of overall arithmetic operations is theoretically bounded by O(n 3 L).
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  • 98
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    Mathematical programming 52 (1991), S. 597-618 
    ISSN: 1436-4646
    Keywords: Integer programming ; interior point method ; Steiner triple systems ; set covering
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We present an interior point approach to the zero–one integer programming feasibility problem based on the minimization of a nonconvex potential function. Given a polytope defined by a set of linear inequalities, this procedure generates a sequence of strict interior points of this polytope, such that each consecutive point reduces the value of the potential function. An integer solution (not necessarily feasible) is generated at each iteration by a rounding scheme. The direction used to determine the new iterate is computed by solving a nonconvex quadratic program on an ellipsoid. We illustrate the approach by considering a class of difficult set covering problems that arise from computing the 1-width of the incidence matrix of Steiner triple systems.
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  • 99
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    Mathematical programming 53 (1992), S. 63-78 
    ISSN: 1436-4646
    Keywords: Quadratic assignment problem ; relaxation ; lower bounds ; eigenvalue decomposition ; steepest ascent
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We investigate new bounding strategies based on different relaxations of the quadratic assignment problem. In particular, we improve the lower bound found by using an eigenvalue decomposition of the quadratic part and by solving a linear program for the linear part. The improvement is accomplished by applying a steepest ascent algorithm to the sum of the two bounds.
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  • 100
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    Mathematical programming 54 (1992), S. 127-153 
    ISSN: 1436-4646
    Keywords: Local minimization ; knapsack ; indefinite ; quadratic knapsack ; complexity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We consider the complexity of finding a local minimum for the nonconvex Quadratic Knapsack Problem. Global minimization for this example of quadratic programming is NP-hard. Moré and Vavasis have investigated the complexity of local minimization for the strictly concave case of QKP; here we extend their algorithm to the general indefinite case. Our main result is an algorithm that computes a local minimum in O(n(logn)2) steps. Our approach involves eliminating all but one of the convex variables through parametrization, yielding a nondifferentiable problem. We use a technique from computational geometry to address the nondifferentiable problem.
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