A Class of Mathematical Programs with Equilibrium Constraints: A Smooth Algorithm and Applications to Contact Problems

We discuss a special mathematical programming problem with equilibrium constraints (MPEC), that arises in material and shape optimization problems involving the contact of a rod or a plate with a rigid obstacle. This MPEC can be reduced to a nonlinear programming problem with independent variables and some dependent variables implicity defined by the solution of a mixed linear complementarity problem (MLCP). A projected-gradient algorithm including a complementarity method is proposed to solve this optimization problem. Several numerical examples are reported to illustrate the efficiency of this methodology in practice

On the Quadratic Eigenvalue Complementarity Problem

Abstract We introduce several new results on the Quadratic Eigenvalue Complementarity Problem (QEiCP), focusing on the nonsymmetric case, i,e, without making symmetry assumptions on the matrices defining the problem. First we establish a new sufficient condition for existence of solutions of this problem, which is somewhat more manageable than previously existent ones. This condition works through the introduction of auxiliary variables which leads to the reduction of QEiCP to an Eigenvalue Complementarity Problem (EiCP) in higher dimension. Hence, this reduction suggests a new strategy for solving QEiCP, which is also analyzed in the paper. We also present an upper bound for the number of solutions of QEiCP and exhibit some examples of instances of QEiCP whose solution set has large cardinality, without attaining though the just mentioned upper bound. We also investigate the numerical solution of the QEiCP by exploiting a nonlinear programming and a variational inequality formulations of QEiCP. Some numerical experiments are reported and illustrate the benefits and drawbacks of using these formulations for solving the QEiCP in practice

Algorithms for linear programming with linear complementarity constraints

Abstract Linear programming with linear complementarity constraints (LPLCC) is an area of active research in Optimization, due to its many applications, algorithms, and theoretical existence results. In this paper, a number of formulations for important nonconvex optimization problems are first reviewed. The most relevant algorithms for computing a complementary feasible solution, a stationary point, and a global minimum for the LPLCC are also surveyed, together with some comments about their efficiency and efficacy in practice

Complementarity and genetic algorithms for an optimization shell problem

The application of complementarity and genetic algorithms to an optimization thin laminated shallow shell problem is discussed. The discrete form of the problem leads to a Mathematical Program with Equilibrium Constraints (MPEC) [1], whose constraint set consists of a variational inequality and a set of equality constraints. Furthermore the variables are discrete. Special instances of the general problem are considered and indicate that the choice of the algorithm depends on the problem to be linear or nonlinear

Generation of Disjointly Constrained Bilinear Programming Test Problems

This paper describes a technique for generating disjointly constrained bilinear programming test problems with known solutions and properties. The proposed construction technique applies a simple random tranformation of variables to a separable bilinear programming problem that is constructed by combining disjoint low-dimensional bilinear programs

A New Technique for Generating Quadratic Programming Test Problems

This paper describes a new technique for generating convex, strictly concave and indefinite (bilinear or not) quadratic programming problems. These problems have a number of properties that make them useful for test purposes. For example, strictly concave quadratic problems with their global maximum in the interior of the feasible domain and with an exponential number of local minima with distinct function values and indefinite and jointly constrained bilinear problems with nonextreme global minima, can be generated. Unlike most existing methods our construction technique does not require the solution of any subproblems or systems of equations. In addition, the authors know of no other technique for generating jointly constrained bilinear programming problems

Cost Minimization of a Multiple Section Power Cable Supplying Several Remote Telecom Equipment

Abstract An optimization problem is described, which arises in telecommunications and is associated to multiple cross sections of a single power cable used to supply remote telecom equipments. The problem consists of minimizing the volume of copper material used in the cables and consequently the total cable cost. Two main formulations for the problem are introduced and some properties of the functions and constraints involved are presented. In particular it is shown that the optimization problems are convex and have a unique optimal solution. A Projected Gradient algorithm is proposed for finding the global minimum of the optimization problem, which takes advantage of the particular structure of the second formulation. An analysis of the performance of the latter algorithm for a given real-life problem is also presented

The eigenvalue complementarity problem

Abstract In this paper an eigenvalue complementarity problem (EiCP) is studied, which finds its origins in the solution of a contact problem in mechanics. The EiCP is shown to be equivalent to a Nonlinear Complementarity Problem, a Mathematical Programming Problem with Complementarity Constraints and a Global Optimization Problem. A finite Reformulation–Linearization Technique (Rlt)-based tree search algorithm is introduced for processing the EiCP via the lattermost of these formulations. Computational experience is included to highlight the efficacy of the above formulations and corresponding techniques for the solution of the EiCP