Solving semidefinite programming problems using branch-and-bound

Many combinatorial optimization problems have relaxations that are semidefinite programming problems. In principle, the combinatorial optimization problem can then be solved by using a branch-and-cut procedure, where the problems to be solved at the nodes of the tree are semidefinite programs. It is desirable that the solution to one node of the tree should be exploited at the child node in order to speed up the solution of the child. We discuss the incorporation of branch-and-cut into solution techniques for semidefinite programming problems. In particular, we show how the solution to the parent relaxation can be used as a warm start to construct an appropriate initial dual solution to the child problem. This restart method for SDP branch-and-cut can be regarded as analogous to the use of the dual simplex method in the branch-and-cut method for mixed integer linear programming problems.