Iterative bundle-based decomposition for large-scale nonseparable convex optimization

There has been considerable research in solving large-scale separable convex optimization problems. In this paper we present an algorithm for large-scale nonseparable smooth convex optimization problems with block-angular linear constraints. The solution of the problem is approximated by solving a sequence of structured separable quadratic programs. The bundle-based decomposition method of Robinson is applied to each separable quadratic program. We implement the algorithm and present computational experience.