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On computation of C-stationary points for equilibrium problems with linear complementarity constraints via homotopy method

Michal Červinka (2010)

Kybernetika

In the paper we consider EPCCs with convex quadratic objective functions and one set of complementarity constraints. For this class of problems we propose a possible generalization of the homotopy method for finding stationary points of MPCCs. We analyze the difficulties which arise from this generalization. Numerical results illustrate the performance for randomly generated test problems.

On quasi-solution to infeasible linear complementarity problem obtained by Lemke’s method

L. Popov (2004)

Open Mathematics

For a linear complementarity problem with inconsistent system of constraints a notion of quasi-solution of Tschebyshev type is introduced. It’s shown that this solution can be obtained automatically by Lemke’s method if the constraint matrix of the original problem is copositive plus or belongs to the intersection of matrix classes P 0 and Q 0.

On semidefinite bounds for maximization of a non-convex quadratic objective over the l1 unit ball

Mustafa Ç. Pinar, Marc Teboulle (2006)

RAIRO - Operations Research

We consider the non-convex quadratic maximization problem subject to the l1 unit ball constraint. The nature of the l1 norm structure makes this problem extremely hard to analyze, and as a consequence, the same difficulties are encountered when trying to build suitable approximations for this problem by some tractable convex counterpart formulations. We explore some properties of this problem, derive SDP-like relaxations and raise open questions.

On the quadratic fractional optimization with a strictly convex quadratic constraint

Maziar Salahi, Saeed Fallahi (2015)

Kybernetika

In this paper, we have studied the problem of minimizing the ratio of two indefinite quadratic functions subject to a strictly convex quadratic constraint. First utilizing the relationship between fractional and parametric programming problems due to Dinkelbach, we reformulate the fractional problem as a univariate equation. To find the root of the univariate equation, the generalized Newton method is utilized that requires solving a nonconvex quadratic optimization problem at each iteration. A...

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