On the Exactness of Dantzig-Wolfe Relaxation for Rank Constrained Optimization Problems

TL;DR

This paper characterizes when Dantzig-Wolfe relaxation exactly solves rank-constrained optimization problems, providing necessary and sufficient conditions for various forms of exactness, thereby extending previous results in QCQP and fair learning.

Contribution

It develops the first known necessary and sufficient conditions for the exactness of Dantzig-Wolfe relaxation in rank-constrained optimization problems.

Findings

Conditions for extreme point exactness established

Conditions for convex hull exactness derived

Conditions for objective value exactness identified

Abstract

In the rank-constrained optimization problem (RCOP), it minimizes a linear objective function over a prespecified closed rank-constrained domain set and $m$ generic two-sided linear matrix inequalities. Motivated by the Dantzig-Wolfe (DW) decomposition, a popular approach of solving many nonconvex optimization problems, we investigate the strength of DW relaxation (DWR) of the RCOP, which admits the same formulation as RCOP except replacing the domain set by its closed convex hull. Notably, our goal is to characterize conditions under which the DWR matches RCOP for any m two-sided linear matrix inequalities. From the primal perspective, we develop the first-known simultaneously necessary and sufficient conditions that achieve: (i) extreme point exactness -- all the extreme points of the DWR feasible set belong to that of the RCOP; (ii) convex hull exactness -- the DWR feasible set is identical to the closed convex hull of RCOP feasible set; and (iii) objective exactness -- the optimal values of the DWR and RCOP coincide. The proposed conditions unify, refine, and extend the existing exactness results in the quadratically constrained quadratic program (QCQP) and fair unsupervised learning. These conditions can be very useful to identify new results, including the extreme point exactness for a QCQP problem that admits an inhomogeneous objective function with two homogeneous two-sided quadratic constraints and the convex hull exactness for fair SVD.