8 research outputs found

    Depth-first simplicial partition for copositivity detection, with an application to MaxClique

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    Detection of copositivity plays an important role in combinatorial and quadratic optimization. Recently, an algorithm for copositivity detection by simplicial partition has been proposed. In this paper, we develop an improved depth-first simplicial partition algorithm which reduces memory requirements significantly and therefore enables copositivity checks of much larger matrices – of size up to a few thousands instead of a few hundreds. The algorithm has been investigated experimentally on a number of MaxClique problems as well as on generated random problems. We present numerical results showing that the algorithm is much faster than a recently published linear algebraic algorithm for copositivity detection based on traditional ideas – checking properties of principal sub-matrices. We also show that the algorithm works very well for solving MaxClique problems through copositivity checks

    LP-based Tractable Subcones of the Semidefinite Plus Nonnegative Cone

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    Tractable Subcones and LP-based Algorithms for Testing Copositivit

    Copositivity tests based on the linear complementarity problem

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    Copositivity tests are presented based on new necessary and suffcient conditions requiring the solution of linear complementarity problems (LCP). Methodologies involving Lemke's method, an enumerative algorithm and a linear mixed-integer programming formulation are proposed to solve the required LCPs. A new necessary condition for (strict) copositivity based on solving a Linear Program (LP) is also discussed, which can be used as a preprocessing step. The algorithms with these three different variants are thoroughly applied to test matrices from the literature and to max-clique instances with matrices up to dimension 496 x 496. We compare our procedures with three other copositivity tests from the literature as well as with a general global optimization solver. The numerical results are very promising and equally good and in many cases better than the results reported elsewhere.Mathematics subject classifications (MSC 2010): 15B48 Positive matrices and their generalizations; cones of matrices 90C33 Complementarity and equilibrium problems and variational inequalities (finite dimensions) 65F30 Other matrix algorithms 65K99 None of the above, but in this section 90C26 Nonconvex programming, global optimizatio
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