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This paper is devoted to difference of convex functions (d.c.) optimization: d.c. duality, local and global optimality conditions in d.c. programming, the d.c. algorithm (DCA), and its application to solving the trust-region problem. The DCA is an iterative method that is quite different from well-known related algorithms. Thanks to the particular structure(More)
In this paper, we investigate a DC (Difference of Convex functions) programming technique for solving large scale Eigenvalue Complementarity Problems (EiCP) with real symmetric matrices. Three equivalent formulations of EiCP are considered. We first reformulate them as DC programs and then using DCA (DC Algorithm) for their solution. Computational results(More)
In this paper, we propose nonlinear programming formulations (NLP) and DC (Difference of Convex functions) programming approaches for the asymmetric eigenvalue complementarity problem (EiCP). The EiCP has a solution if and only if these NLPs have zero global optimal value. We reformulate the NLPs as DC Programs (DCP) which can be efficiently solved by DCA(More)