General Linear Complementarity Problem Research Articles

Predictor-Corrector Interior-Point Algorithm for the General Linear Complementarity Problem

Abstract We study a predictor-corrector interior-point algorithm for solving general linear complementarity problems from the implementation point of view. We analyze the method proposed by Illés, Nagy and Terlaky [1] that extends the algorithm published by Potra and Liu [2] to general linear complementarity problems. A new method for determining the step size of the corrector direction is presented. Using the code implemented in the C++ programming language, we can solve large-scale problems based on sufficient matrices.

Numerical Results for the General Linear Complementarity Problem

Abstract In this article we discuss the interior-point algorithm for the general complementarity problems (LCP) introduced by Tibor Illés, Marianna Nagy and Tamás Terlaky. Moreover, we present a various set of numerical results with the help of a code implemented in the C++ programming language. These results support the efficiency of the algorithm for both monotone and sufficient LCPs.

On Solving Mean Payoff Games Using Pivoting Algorithms

Two classical pivoting algorithms, due to Lemke and Cottle–Dantzig, are studied on linear complementarity problems (LCPs) and their generalizations that arise from infinite duration two-person mean payoff games (MPGs) under zero-mean partition problem. Lemke’s algorithm was studied in solving MPGs via reduction to discounted payoff games or to simple stochastic games. We provide an alternative and efficient approach for studying the LCPs arising from the MPGs without any reduction. A binary MPG can easily be formulated as an LCP which has always terminated in a complementary solution in numerical experiments, but has not yet been proven either the processability of MPG’s by Lemke’s algorithm or a counter example that it will not terminate with a solution. Till now, the processability of MPG’s by Lemke’s algorithm remains open. A general MPG (with arbitrary outgoing arcs) naturally reduces to a generalized linear complementarity problem (GLCP) involving a rectangular matrix where the vertices are represented by the columns and the outgoing arcs from each vertex are represented by rows in a particular block. The noteworthy result in this paper is that the GLCP obtained from an MPG is processable by Cottle–Dantzig principal pivoting algorithm which terminates with a solution. Several properties of the matrix which arise in this context are also discussed.

Direct solution of piecewise linear systems

Let S be a real n×n matrix, z,cˆ∈Rn, and |z| the componentwise modulus of z. Then the piecewise linear equation systemz−S|z|=cˆ is called an absolute value equation (AVE). It has been proven to be equivalent to the general linear complementarity problem, which means that it is NP-hard in general.We will show that for several system classes (in the sense of structural impositions on S) the AVE essentially retains the good-natured solvability properties of regular linear systems. I.e., it can be solved directly by a slightly modified Gaussian elimination that we call the signed Gaussian elimination. For dense matrices S this algorithm has, up to a term in O(n), the same operations count as the classical Gaussian elimination with column pivoting. For tridiagonal systems in n variables its computational cost is roughly that of sorting n floating point numbers. The sharpness of the proposed restrictions on S will be established.

Enumeration of PLCP-orientations of the 4-cube

The linear complementarity problem (LCP) provides a unified approach to many problems such as linear programs, convex quadratic programs, and bimatrix games. The general LCP is known to be NP-hard, but there are some promising results that suggest the possibility that the LCP with a P-matrix (PLCP) may be polynomial-time solvable. However, no polynomial-time algorithm for the PLCP has been found yet and the computational complexity of the PLCP remains open. Simple principal pivoting (SPP) algorithms, also known as Bard-type algorithms, are candidates for polynomial-time algorithms for the PLCP. In 1978, Stickney and Watson interpreted SPP algorithms as a family of algorithms that seek the sink of unique-sink orientations of n-cubes. They performed the enumeration of the arising orientations of the 3-cube, hereafter called PLCP-orientations. In this paper, we present the enumeration of PLCP-orientations of the 4-cube. The enumeration is done via construction of oriented matroids generalizing P-matrices and realizability classification of oriented matroids. Some insights obtained in the computational experiments are presented as well.

The Solution Structure and Error Estimation for The Generalized Linear Complementarity Problem

In this paper, we consider the generalized linear complementarity problem (GLCP). Firstly, we develop some equivalent reformulations of the problem under milder conditions, and then characterize the solution of the GLCP. Secondly, we also establish the global error estimation for the GLCP by weakening the assumption. These results obtained in this paper can be taken as an extension for the classical linear complementarity problems.

SOLVING STRONGLY MONOTONE LINEAR COMPLEMENTARITY PROBLEMS

Given a linear transformation L on a finite dimensional real inner product space V to itself and an element q ∈ V we consider the general linear complementarity problem LCP (L, K, q) on a proper cone K ⊆ V. We observe that the iterates generated by any closed algorithmic map will converge to a solution for LCP (L, K, q), whenever L is strongly monotone. Lipschitz constants of L is vital in establishing the above said convergence. Hence we compute the Lipschitz constants for certain classes of Lyapunov, Stein and double-sided multiplicative transformations in the setting of semidefinite linear complementarity problems. We give a numerical illustration of a closed algorithmic map in the setting of a standard linear complementarity problem. On account of the difficulties in numerically implementing such algorithms for general linear complementarity problems, we give an alternative algorithm for computing the solution for a special class of strongly monotone semidefinite linear complementarity problems along with a numerical example.

Granular contact dynamics using mathematical programming methods

A class of variational formulations for discrete element analysis of granular media is presented. These formulations lead naturally to convex mathematical programs that can be solved using standard and readily available tools. In contrast to traditional discrete element analysis, the present granular contact dynamics formulation uses an implicit time discretization, thus allowing for large time steps. Moreover, in the limit of an infinite time step, the general dynamic formulation reduces to a static formulation that is useful in simulating common quasi-static problems such as triaxial tests and similar laboratory experiments. A significant portion of the paper is dedicated to exploring the consequences of the associated frictional sliding rule implied by the variational formulation adopted. In this connection, a new interior-point algorithm for general linear complementarity problems is developed and it is concluded that the associated sliding rule, in the context of granular contact dynamics, may be viewed as an artifact of the time discretization and that the use of an associated flow rule at the particle scale level generally is physically acceptable.

Existence of optimal solutions for general stochastic linear complementarity problems

In this paper we show the solvability of the expected residual minimization (ERM) formulation for the general stochastic linear complementarity problem (SLCP) under mild assumptions. The properties of the ERM formulation are dependent on the choice of NCP functions. We focus on the ERM formulations defined by the “min” NCP function and the penalized FB function, both of which are nonconvex programs on the nonnegative orthant.

A method for solving the general parametric linear complementarity problem

This paper presents a solution method for the general (mixed integer) parametric linear complementarity problem pLCP(q(θ),M), where the matrix M has a general structure and integrality restriction can be enforced on the solution. Based on the equivalence between the linear complementarity problem and mixed integer feasibility problem, we propose a mixed integer programming formulation with an objective of finding the minimum 1-norm solution for the original linear complementarity problem. The parametric linear complementarity problem is then formulated as multiparametric mixed integer programming problem, which is solved using a multiparametric programming algorithm. The proposed method is illustrated through a number of examples.

A new type of solution method for the generalized linear complementarity problem over a polyhedral cone

This paper addresses the generalized linear complementarity problem (GLCP) over a polyhedral cone. To solve the problem, we first equivalently convert the problem into an affine variational inequalities problem over a closed polyhedral cone, and then propose a new type of method to solve the GLCP based on the error bound estimation. The global and R-linear convergence rate is established. The numerical experiments show the efficiency of the method.

An extension of interior point potential reduction algorithm to solve general LCPs

Linear complementarity problems (LCP) may be solved if their condition number, defined appropriately is positive. This paper presents a transformation for the LCP which satisfy mild conditions to a form which will have a positive condition number. For these extensive classes an Interior point method can be used to solve it.

A polynomial path-following interior point algorithm for general linear complementarity problems

Linear Complementarity Problems (LCPs) belong to the class of $${\mathbb{NP}}$$ -complete problems. Therefore we cannot expect a polynomial time solution method for LCPs without requiring some special property of the coefficient matrix. Our aim is to construct interior point algorithms which, according to the duality theorem in EP (Existentially Polynomial-time) form, in polynomial time either give a solution of the original problem or detects the lack of property $${\mathcal{P}_*(\tilde\kappa)}$$ , with arbitrary large, but apriori fixed $${\tilde\kappa}$$ ). In the latter case, the algorithms give a polynomial size certificate depending on parameter $${\tilde{\kappa}}$$ , the initial interior point and the input size of the LCP). We give the general idea of an EP-modification of interior point algorithms and adapt this modification to long-step path-following interior point algorithms.

COAP 2007 Best Paper Award

Each year, the Computational Optimization and Applications (COAP) editorial board selects a paper from the preceding year’s COAP publications for the Best Paper Award. The recipient of the award for papers published in 2007 is Olvi Mangasarian of the University of Wisconsin, Madison and the University of California, San Diego, for his paper “Absolute Value Programming”, published in Volume 36, pages 43–53. This paper [7] as well as subsequent closely related papers [6, 8, 9] deal with the absolute value equation (AVE) Ax + B|x| = b, where A and B are arbitrary m× n real matrices and |x| denotes the vector with absolute values of the n-dimensional real valued vector x. The significance of this class of NP-hard problems arises partly from the fact that when B = I , the identity matrix, AVE is equivalent to the general linear complementarity problem, 0 ≤ x ⊥Mx + q ≥ 0. Even though problems involving absolute values are NP-hard, they share some very interesting properties with those of linear systems. For example, optimization problems with absolute value constraints possess optimality and duality results similar to those of linear programming, even though the problems are inherently nonconvex. Another interesting property that AVE shares with linear inequalities are theorems of the alternative which are established in this paper. The paper also contains a finite successive linearization algorithm for solving absolute value equations that terminates at a necessary optimality condition. This algorithm has solved all random test problems given to it for which mostly m ≥ 2n or n ≥ 2m, up to size (m,n) of (2000,100) and (100,2000). When m = n and B is invertible, which is the case for the linear complementarity problem formulation, a simpler concave minimization

A quadratically convergent algorithm for the generalized linear complementarity problem

In this paper, the generalized linear complementarity problem (GLCP) is reformulated as a system of nonsmooth equations via the Fischer function. Based on this reformulation, the famous Levenberg-Marquardt(LM) algorithm is employed for obtaining its solution. Theoretical results that relate the stationary points of the merit function to the solution of the GLCP are presented. We show that the L-M algorithm is both globally and Quadratically convergent without nondegenerate solution. Moreover, a method to calculate a generalized Jacobian is also given. Mathematics Subject Classifications: 65H10, 90C33, 90C30

Absolute value programming

We investigate equations, inequalities and mathematical programs involving absolute values of variables such as the equation Ax+B|x| = b, where A and B are arbitrary m× n real matrices. We show that this absolute value equation is NP-hard to solve, and that solving it with B = I solves the general linear complementarity problem. We give sufficient optimality conditions and duality results for absolute value programs as well as theorems of the alternative for absolute value inequalities. We also propose concave minimization formulations for absolute value equations that are solved by a finite succession of linear programs. These algorithms terminate at a local minimum that solves the absolute value equation in almost all solvable random problems tried.

A class of polynomially solvable linear complementarity problems

Although the general linear complementarity problem (LCP) is NP-complete, there are special classes that can be solved in polynomial time. One example is the type where the defining matrix is nondegenerate and for which the n-step property holds. In this paper we consider an extension of the property to the degenerate case by introducing the concept of an extended n-step vector and matrix. It is shown that the LCP defined by such a matrix is polynomially solvable as well.

Unified approaches for solvable and unsolvable linear complementarity problems

In this paper, general linear complementarity problems (LCPs) are studied via global optimization problems. In particular, unsolvable LCPs are reformulated as multicriteria optimization, minimax optimization and quadratic programming problems. The solvability and unsolvability of LCPs are obtained via these reformulations. Furthermore, first-order and second-order global optimality conditions of LCPs are derived. Some examples are also given to demonstrate these optimality conditions.

Solution of a General Linear Complementarity Problem Using Smooth Optimization and Its Application to Bilinear Programming and LCP

This paper addresses a General Linear Complementarity Problem (GLCP) that has found applications in global optimization. It is shown that a solution of the GLCP can be computed by finding a stationary point of a differentiable function over a set defined by simple bounds on the variables. The application of this result to the solution of bilinear programs and LCPs is discussed. Some computational evidence of its usefulness is included in the last part of the paper.

Minimum-support solutions of polyhedral concave programs*

Motivated by the successful application of mathematical programming techniques to difficult machine learning problems, we seek solutions of concave minimization problems over polyhedral sets with minimum number of nonzero components. We that if such problems have a solution, they have a vertex solution with a minimal number of zeros. This includes linear programs and general linear complementarity problems. A smooth concave exponential approximation to a step function solves the minimumsupport problem exactly for a finite value of the smoothing parameter. A fast finite linear-programming-based iterative method terminates at a stationary point, which for many important real world problems provides very useful answers. Utilizing the complementarity property of linear programs and linear complementarity problems, an upper bound on the number of nonzeros can be obtained by solving a single convex minimization problem on a polyhedral set