Self-Scaled Cones and Interior-Point Methods in Nonlinear Programming
AbstractThis paper provides a theoretical foundation for efficient interior-point algorithms for nonlinear programming problems expressed in conic form, when the cone and its associated barrier are self-scaled. For such problems we devise long-step and symmetric primal-dual methods. Because of the special properties of these cones and barriers, our algorithms can take steps that go typically a large fraction of the way to the boundary of the feasible region, rather than being confined to a ball of unit radius in the local norm defined by the Hessian of the barrier.
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1994062.
Date of creation: 01 Nov 1994
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Nonlinear programming; conical form; interior point algorithms; selfconcordant barrier; self-scaled cone; self-scaled barrier; path-following algorithms; potential-reduction algorithms;
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