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Nili Guttmann-Beck

FOM — A MATLAB Toolbox of First Order Methods for Solving Convex Optimization Problems

Published: 2017/08/30
  • Amir Beck
  • Nili Guttmann-Beck
  • Categories Optimization Software and Modeling Systems

    This paper presents the FOM MATLAB toolbox for solving convex optimization problems using first order methods. The diverse features of the eight solvers included in the package are illustrated through a collection of examples of different nature. ArticleDownload View PDF

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    Keywords

    alternating direction method of multipliers augmented lagrangian method benders decomposition bilevel optimization Branch-and-Bound branch-and-cut chance constraints column generation combinatorial optimization complexity convergence rate 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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