‘Multidimensional’ extensions and a nested dual approach for the m-median problem

A general framework for modeling median type locational decisions, where (i) travel costs and demands may be stochastic, (ii) multiple services or commodities need to be considered, and/or (iii) multiple median type objectives might exist, is presented—using the concept of “multidimensional networks”. The classical m-median problem, the stochastic m-median problem, the multicommodity m-median problem and and multiobjective m-median problem are defined within this framework.By an appropriate transformation of variables, the multidimensional m-median problem simplifies to the classical m-median problem but with a K-fold increase in the number of nodes, where K is the number of dimensions of the network. A nested dual approach to solve the resulting classical m-median problem, that uses Erlenkotter's facility location scheme as a subroutine, is presented. Computational results indicate that the procedure may perhaps be the best available one to solve the m-median problem exactly.     

Computable numerical bounds for lagrange multipliers of stationary points of non-convex differentiable non-linear programs

It is shown that the satisfaction of a standard constraint qualification of mathematical programming [5] at a stationary point of a non-convex differentiable non-linear program provides explicit numerical bounds for the set of all Lagrange multipliers associated with the stationary point. Solution of a single linear program gives a sharper bound together with an achievable bound on the 1-norm of the multipliers associated with the inequality constraints. The simplicity of obtaining these bounds contrasts sharply with the intractable NP-complete problem of computing an achievable upper bound on the p-norm of the multipliers associated with the equality constraints for integer $\text{p ≧ 1}$.     

Principal direction search: A new method of search for unconstrained LP formulations

In this paper we establish a theoretical background that allows constrained LP formulations of some well-known minimum location problems to be transformed to equivalent unconstrained ones. Based on these unconstrained formulations a new and very promising method of search for optimal solutions, is theoretically established and algorithmicly developed. Numerical experience is reported.     

Lagrange multiplers for pareto nonsmooth programming problems in banach spaces

We establish the existence of Lagrange multipliers for general Pareto multiobjective mathematical programming problems in Banach spaces. Here the data are general nonsmooth strongly compactly ipschitzian mappings     

Zero-one integer programs with few constraints —Lower bounding theory

The zero-one integer programming problem and its special case, the multiconstraint knapsack problem frequently appear as subproblems in many combinatorial optimization problems. We present several methods for computing lower bounds on the optimal solution of the zero-one integer programming problem. They include Lagrangean, surrogate and composite relaxations. New heuristic procedures are suggested for determining good surrogate multipliers. Based on theoretical results and extensive computational testing, it is shown that for zero-one integer problems with few constraints surrogate relaxation is a viable alternative to the commonly used Lagrangean and linear programming relaxations. These results are used in a follow up paper to develop an efficient branch and bound algorithm for solving zero-one integer programming problems.     

On geometric programming and complementary slackness

When the terms in a convex primal geometric programming (GP) problem are multiplied by slack variables whose values must be at least unity, the invariance conditions may be solved as constraints in a linear programming (LP) problem in logarithmically transformed variables. The number of transformed slack variables included in the optimal LP basis equals the degree of difficulty of the GP problem, and complementary slackness conditions indicate required changes in associated GP dual variables. A simple, efficient search procedure is used to generate a sequence of improving primal feasible solutions without requiring the use of the GP dual objective function. The solution procedure appears particularly advantageous when solving very large geometric programming problems, because only the right-hand constants in a system of linear equations change at each iteration.The influence of J. G. Ecker, the writer's teacher, is present throughout this paper. Two anonymous referees and the Associate Editor made very helpful suggestions. Dean Richard W. Barsness provided generous support for this work.     

Lagrange multipliers theorem and saddle point optimality criteria in mathematical programming

We prove a version of Lagrange multipliers theorem for nonsmooth functionals defined on normed spaces. Applying these results, we extend some results about saddle point optimality criteria in mathematical programming.     

Lagrange Multipliers and saddle points in multiobjective programming

In this paper, we present several conditions for the existence of a Lagrange multiplier or a weak saddle point in multiobjective optimization. Relations between a Lagrange multiplier and a weak saddle point are established. A sufficient condition is also given for the equivalence of the Benson proper efficiency and the Borwein proper efficiency.This research was supported by NSFC under Grant No. 78900011 and by BMADIS. The authors are grateful to two referees for supplying valuable comments and pointing out detailed corrections to the draft paper. The authors also wish to thank Dr. P. L. Yu for valuable comments and suggestions.The revised version of this paper was completed while the second author visited the Faculty of Technical Mathematics and Informatics, Delft University of Technology, Delft, The Netherlands.     

Preparation of papers

Recent studies are concerned with two types of questions in nonconvex optimization: (a) conditions for having bounded Lagrange multipliers, Refs. 1–2; (b) a priori bounds for such Lagrange multipliers, Ref. 3. Such topics have been investigated under suitable regularity assumptions. The purpose of this paper is to study the same problems for the generalized Lagrange multipliers of a locally Lipschitz programming.The author thanks the referees for helpful suggestions     

On programming when the positive cone has an empty interior

In this note, we present a condition which is equivalent to the existence of the Lagrange multiplier for the general convex programming problem. This condition enables one to study a hypothesis distinct from the one of nonempty interior of the positive cone of the space of restrictions that is commonly used. Simple examples of this condition are given. We also explore the relationship of this condition with the subdifferentiability of the primal functional.     

Improving benders decomposition using a genetic algorithm

We develop and investigate the performance of a hybrid solution framework for solving mixed-integer linear programming problems. Benders decomposition and a genetic algorithm are combined to develop a framework to compute feasible solutions. We decompose the problem into a master problem and a subproblem. A genetic algorithm along with a heuristic are used to obtain feasible solutions to the master problem, whereas the subproblem is solved to optimality using a linear programming solver. Over successive iterations the master problem is refined by adding cutting planes that are implied by the subproblem. We compare the performance of the approach against a standard Benders decomposition approach as well as against a stand-alone solver (Cplex) on MIPLIB test problems.     

On the convergence of cross decomposition

Cross decomposition is a recent method for mixed integer programming problems, exploiting simultaneously both the primal and the dual structure of the problem, thus combining the advantages of Dantzig—Wolfe decomposition and Benders decomposition. Finite convergence of the algorithm equipped with some simple convergence tests has been proved. Stronger convergence tests have been proposed, but not shown to yield finite convergence.In this paper cross decomposition is generalized and applied to linear programming problems, mixed integer programming problems and nonlinear programming problems (with and without linear parts). Using the stronger convergence tests finite exact convergence is shown in the first cases. Unbounded cases are discussed and also included in the convergence tests. The behaviour of the algorithm when parts of the constraint matrix are zero is also discussed. The cross decomposition procedure is generalized (by using generalized Benders decomposition) in order to enable the solution of nonlinear programming problems.     

The lagrange multiplier set and the generalized gradient set of the marginal function of a differentiable program in a Banach space

We prove that, under the usual constraint qualification and a stability assumption, the generalized gradient set of the marginal function of a differentiable program in a Banach space contains the Lagrange multiplier set. From there, we deduce a sufficient condition in order that, in finite-dimensional spaces, the Lagrange multiplier set be equal to the generalized gradient set of the marginal function.The author wishes to thank J. B. Hiriart-Urruty for many helpful suggestions during the preparation of this paper. He also wishes to express his appreciation to the referees for their many valuable comments.     

Constrained optimization using multiple objective programming

In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches are specifically designed to incorporate such complex preference structures, they gain more and more importance in application areas as, for example, engineering design and capital budgeting. The aim of this paper is to analyze optimization problems both from a constrained programming and a multicriteria programming perspective. It is shown that both formulations share important properties, and that many classical solution approaches have correspondences in the respective models. The analysis naturally leads to a discussion of the applicability of some recent approximation techniques for multicriteria programming problems for the approximation of optimal solutions and of Lagrange multipliers in convex constrained programming. Convergence results are proven for convex and nonconvex problems.     

On the existence and nonexistence of Lagrange multipliers in Banach spaces

The paper deals with the existence of Lagrange multipliers for a general nonlinear programming problem. Some regularity conditions are formulated which are, in a sense, the weakest to assure the existence of multipliers. A number of related conditions are discussed. The connection between the choice of suitable function spaces and the existence of multipliers is analyzed.This work was partly supported by the National Science Foundation, Grant No. GF-37298, to the Institute of Automatic Control, Technical University of Warsaw, Warsaw, Poland, and the Department of Computer and Control Sciences, University of Minnesota, Minneapolis, Minnesota.The author wishes to thank Professor A. P. Wierzbicki for many important remarks concerning the subject of this paper.     

A regularized domain decomposition method with Lagrange multiplier

In this paper, we are concerned with the nonoverlapping domain decomposition method with Lagrange multiplier for three-dimensional second-order elliptic problems with no zeroth-order term. It is known that the methods result in a singular subproblem on each internal (floating) subdomain. To handle the singularity, we propose a regularization technique which transforms the corresponding singular problems into approximate positive definite problems. For the regularized method, one can build the interface equation of the multiplier directly. We first derive an optimal error estimate of the regularized approximation, and then develop a cheap preconditioned iterative method for solving the interface equation. For the new method, the cost of computation will not be increased comparing the case without any floating subdomain. The effectiveness of the new method will be confirmed by both theoretical analyzes and numerical experiments. The work is supported by Natural Science Foundation of China G10371129.     

A remark on a regularity assumption for the mathematical programming problem in Banach spaces

This paper deals with a recently proposed Slater-like regularity condition for the mathematical programming problem in infinite-dimensional vector spaces (Ref. 1). The attractive feature of this constraint qualification is the fact that it can be considered as a condition only on theactive part of the constraint. We prove that the studied regularity condition is equivalent to the regularity assumption normally used in the study of the mathematical programming problem in infinite-dimensional vector spaces.     

An economic interpretation of exact penalty functions using sensitivity analysis

It has already been demonstrated that under some assumptions, a local minimum of a constrained problem is also a local unconstrained minimum of a function which is called an exact penalty function. Here, we present the same result with a new demonstration. By using sensitivity analysis, we give an economic interpretation for exact penalty functions.     

Lagrangian formulation of domain decomposition methods: A unified theory

Domain decomposition methods based on one Lagrange multiplier have been shown to be very efficient for solving ill-conditioned problems in parallel. Several variants of these methods have been developed in the last ten years. These variants are based on an augmented Lagrangian formulation involving one or two Lagrange multipliers and on mixed type interface conditions between the sub-domains. In this paper, the Lagrangian formulations of some of these domain decomposition methods are presented both from a continuous and a discrete point of view.     

On the convergence rate for a penalty function method of exponential type

Recently, Kort and Bertsekas (Ref. 1) and Hartman (Ref. 2) presented independently a new penalty function algorithm of exponential type for solving inequality-constrained minimization problems. The main purpose of this work is to give a proof on the rate of convergence of a modification of the exponential penalty method proposed by these authors. We show that the sequence of points generated by the modified algorithm converges to the solution of the original nonconvex problem linearly and that the sequence of estimates of the optimal Lagrange multiplier converges to this multiplier superlinearly. The question of convergence of the modified method is discussed. The present paper hinges on ideas of Mangasarian (Ref. 3), but the case considered here is not covered by Mangasarian's theory.