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 (BBD) method of Robinson (In: Prekopa, A., Szelezsan, J., Strazicky, B. (Eds.), System Modelling and Optimization, Springer, 1986, pp. 751-756; Annals de Institute Henri Poincare: Analyse Non Lineaire 6 (1989) 435-447) is applied to each separable quadratic program. We implement the algorithm and present computational experience. (C) 1998 Elsevier Science B.V. All rights reserved.