Block Factor-Width-Two Matrices in Semidefinite Programming
This work provides a new tool for scaling semidefinite programming to larger problems, particularly in sum-of-squares optimization.
The paper introduces block factor-width-two matrices, a generalization of factor-width-two matrices that form a proper cone with a closed-form dual cone. These cones enable decomposition of large semidefinite constraints into smaller ones, demonstrated on sum-of-squares optimization problems.
In this paper, we introduce a set of block factor-width-two matrices, which is a generalisation of factor-width-two matrices and is a subset of positive semidefinite matrices. The set of block factor-width-two matrices is a proper cone and we compute a closed-form expression for its dual cone. We use these cones to build hierarchies of inner and outer approximations of the cone of positive semidefinite matrices. The main feature of these cones is that they enable a decomposition of a large semidefinite constraint into a number of smaller semidefinite constraints. As the main application of these classes of matrices, we envision large-scale semidefinite feasibility optimisation programs including sum-of-squares (SOS) programs. We present numerical examples from SOS optimisation showcasing the properties of this decomposition.