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Semidefinite programming relaxations of combinatorial optimization problems are often tighter than linear programming relaxations. We describe some research in using SDP relaxations to find exact solutions to combinatorial optimization problems. Semidefinite programs are expensive to solve directly, so we also consider cutting surface approaches to solving them.
Finally, we look at recent smoothing techniques for solving nonsmooth optimization problems using a subgradient approach; these methods have some links to cutting surface approaches.
Keywords: interior point column generation, cutting planes, cutting surfaces, semidefinite programming, subgradients
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