On Relation Between Constraint Propagation and Block-Coordinate Descent in Linear Programs

5Citations
Citations of this article
1Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Block-coordinate descent (BCD) is a popular method in large-scale optimization. Unfortunately, its fixed points are not global optima even for convex problems. A succinct characterization of convex problems optimally solvable by BCD is unknown. Focusing on linear programs, we show that BCD fixed points are identical to fixed points of another method, which uses constraint propagation to detect infeasibility of a system of linear inequalities in a primal-dual loop (a special case of this method is the Virtual Arc Consistency algorithm by Cooper et al.). This implies that BCD fixed points are global optima iff a certain propagation rule decides feasibility of a certain class of systems of linear inequalities.

Cite

CITATION STYLE

APA

Dlask, T., & Werner, T. (2020). On Relation Between Constraint Propagation and Block-Coordinate Descent in Linear Programs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12333 LNCS, pp. 194–210). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58475-7_12

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free