Feasibility and Constraint Analysis of Sets of Linear Matrix Inequalities

We present a constraint analysis methodology for Linear Matrix Inequality (LMI) constraints. If the constraint set is found to be feasible we search for a minimal representation; otherwise, we search for an irreducible infeasible system. The work is based on the solution of a set covering problem where each row corresponds to a sample point and is determined by constraint satisfaction at the sampled point. Thus, an implementation requires a method to collect points in the ambient space and a constraint oracle. Much of this paper will be devoted to the development of a hit and run sampling methodology. Test results confirm that our approach not only provides information required for constraint analysis, but will also, if the feasible region has a non-void interior, with probability one, find a feasible point.