Official Course Description
Discrete optimization is a branch of optimization in applied mathematics. As opposed to continuous optimization, the variables used in the mathematical program (or some of them) are restricted to assume only discrete values, such as integers. The course will focus on recent methods for solving large discrete optimization problems. Content: Pseudo boolean optimization, minimization of submodular functions, graph cuts, relaxation techniques (linear programming and semidefinite programming). Applications include markov random fields and standard graph problems (vertex cover, set cover, matching and more).