Miguel F. Anjos, PhD, FCAE
[email protected]

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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

  • 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

  • E. Adams, M.F. Anjos, F. Rendl, and A. Wiegele. A Hierarchy of Subgraph Projection-Based Semide finite Relaxations for some NP-Hard Graph Optimization Problems. To appear in INFOR (accepted September 2015)

  • B. Ghaddar, J.C. Vera, and M.F. Anjos. A Dynamic Inequality Generation Scheme for Polynomial Programming. Mathematical Programming, DOI: 10.1007/s10107-015-0870-9

This paper earned Engau the 2009 MITACS Best Student Paper Award.

This paper earned Ghaddar the 2008 Fraser Research Prize for the Best Research Paper by a graduate student in Management Sciences at the University of Waterloo.

This paper was a Top Cited Paper in the journal Discrete Optimization for the period 2005-2010.