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mathematical programming formulations

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Published: 2018/11/18, Updated: 2019/11/01
  • Bernard Knueven
  • James Ostrowski
  • Jean-Paul Watson
  • Categories Applications - OR and Management Sciences Tags mathematical programming formulations, mixed-integer programming, unit commitment

    CitationDepartment of Industrial and Systems Engineering University of Tennessee, Knoxville, TN 37996 November 2018ArticleDownload 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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