Primal-dual potential reduction algorithm for symmetric programming problems with nonlinear objective functions

We consider a primal-dual potential reduction algorithm for nonlinear convex optimization problems over symmetric cones. The same complexity estimates as in the case of the linear objective function are obtained provided a certain nonlinear system of equations can be solved with a given accuracy. Th...

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Veröffentlicht in:	Linear algebra and its applications 2018-01, Vol.536, p.228-249
1. Verfasser:	Faybusovich, Leonid
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computational geometry Cones Convex objective functions Convexity Linear algebra Lipschitz condition Nonlinear equations Nonlinear systems Optimization Optimization over symmetric cones Quantum entropy Reduction Scaling
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