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symmetric relaxations

Jordan-algebraic aspects of optimization:randomization

Published: 2007/07/07
  • Leonid Faybusovich
  • Categories Linear, Cone and Semidefinite Programming Tags randomization, symmetric relaxations

    We describe a version of randomization technique within a general framework of Euclidean Jordan algebras. It is shown how to use this technique to evaluate the quality of symmetric relaxations for several nonconvex optimization problems CitationPreprint, June 2007ArticleDownload View PDF

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    alternating direction method of multipliers approximation algorithms augmented lagrangian method bilevel optimization Branch-and-Bound branch-and-cut chance constraints column generation combinatorial optimization complexity conic optimization convex optimization cutting planes decomposition derivative-free optimization distributionally robust optimization duality dynamic programming first-order methods global convergence global optimization heuristics integer programming interior point methods large-scale optimization linear programming machine learning mixed-integer linear programming mixed-integer nonlinear programming mixed-integer programming multiobjective optimization nonconvex optimization nonlinear optimization nonlinear programming nonsmooth optimization optimal control optimization proximal point algorithm quadratic programming robust optimization semidefinite programming stochastic optimization stochastic programming trust-region methods unconstrained optimization

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