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Conic optimization is a generalization of linear optimization. My research in this area is concerned with improving models and algorithms for the application of conic optimization to hard engineering optimization problems of a combinatorial nature.

The main objective is to use conic optimization in order to obtain not only good solutions, but also tight bounds on the objective value of the unknown global optimal solution, which are essential to estimate the quality of the solutions found. These ingredients are the key to developing more efficient algorithms for solving these hard problems.


Book Chapters

  • E. Adams and M.F. Anjos. Exact Separation of k-Projection Polytope Constraints. Accepted for publication in: Modeling and Optimization: Theory and Applications, M. Takáč et al. (eds.)
  • M.F. Anjos and J.B. Lasserre. Introduction to Semidefinite, Conic and Polynomial Optimization. In: Handbook on Semidefinite, Cone and Polynomial Optimization, M.F. Anjos and J.B. Lasserre (eds), International Series in Operations Research & Management Science, Frederick S. Hilier (ed.), Springer, 2012, 1-22

Research Articles

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