Basis pursuit by inconsistent alternating projections

TL;DR

This paper introduces a novel alternating projections method for basis pursuit that enforces inconsistency to accelerate convergence, demonstrating linear convergence and competitive performance with existing solvers.

Contribution

It develops a direct $ ext{l}_1$-minimization approach using inconsistent alternating projections, avoiding LP reformulation and proving linear convergence.

Findings

The method converges linearly to the optimal value.

Sequences converge linearly when the solution is unique.

Numerical results show competitiveness with state-of-the-art solvers.

Abstract

Basis pursuit is the problem of finding a vector with smallest $\ell_1$-norm among the solutions of a given linear system of equations. It is a well-known convex relaxation of the sparse affine feasibility problem, where sparse solutions to underdetermined systems are sought. Since basis pursuit admits a linear programming reformulation, standard LP solvers are directly applicable. We instead address the basis pursuit directly in its $\ell_1$-minimization form, without LP reformulation, via a scheme that uses alternating projections in its subproblems. These subproblems are designed to be inconsistent in the sense that they relate to two non-intersecting sets. Recently in [R. Behling, Y. Bello-Cruz and L.-R. Santos, SIAM J. Optim., 31 (2021), pp. 2863-2892], inconsistency coming from infeasibility has been shown to accelerate convergence of alternating projections. We deliberately enforce this inconsistency by constructing subproblems whose feasible sets are disjoint by design. We prove that the resulting $\ell_1$-radii converge linearly to the optimal value, and that when the solution is unique, all generated sequences converge linearly to it at a rate governed by a natural error bound between the feasible set and the optimal $\ell_1$-ball. The proposed method is numerically competitive against state-of-the-art open-source solvers on synthetic and real-world instances.