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Optimization

globaloptimization_1000.gifAlthough with a bias toward continuous optimization problems, the group is also working on discrete optimization and the connection between continuous and discrete optimization. The experience of the group includes, but  is not limited to: interior-point methods for convex problems, stochastic programming, decomposition approaches for structured problems (using specialized interior-point methods, Benders decomposition, dual decomposition, etc.), mixed-integer linear and nonlinear problems and heuristics, among others.

Additionally, the group has experience in the field of deterministic global optimization. Any global optimization method must address the question of how to trascend a given feasible point, if there is one, or else how to produce evidence that the given point is already global one. Global optimization techniques are substantially different from local ones and, among others employ combinatorial tools such as cutting-plane, branch ann bound, branch and cut and so on. The experience of the group in the field of  deterministic global optimization include methods which rely on a d.c. structure of a d.c. program and the cutting angle method of Andramonov and Rubinov for solving Lipschitz  programs.