Templates for Convex Cone Problems with Applications to Sparse Signal Recovery

TL;DR

This paper introduces a flexible, unified framework for solving convex cone problems in signal processing and related fields, enabling efficient solutions for various models including compressed sensing and total variation minimization.

Contribution

It presents a general conic formulation approach with novel technical methods, leading to stable, efficient algorithms applicable to a wide range of convex problems.

Findings

Competitive with state-of-the-art methods for LASSO

Able to solve the Dantzig selector problem efficiently

Provides a software suite for building customized algorithms

Abstract

This paper develops a general framework for solving a variety of convex cone problems that frequently arise in signal processing, machine learning, statistics, and other fields. The approach works as follows: first, determine a conic formulation of the problem; second, determine its dual; third, apply smoothing; and fourth, solve using an optimal first-order method. A merit of this approach is its flexibility: for example, all compressed sensing problems can be solved via this approach. These include models with objective functionals such as the total-variation norm, ||Wx||_1 where W is arbitrary, or a combination thereof. In addition, the paper also introduces a number of technical contributions such as a novel continuation scheme, a novel approach for controlling the step size, and some new results showing that the smooth and unsmoothed problems are sometimes formally equivalent. Combined with our framework, these lead to novel, stable and computationally efficient algorithms. For instance, our general implementation is competitive with state-of-the-art methods for solving intensively studied problems such as the LASSO. Further, numerical experiments show that one can solve the Dantzig selector problem, for which no efficient large-scale solvers exist, in a few hundred iterations. Finally, the paper is accompanied with a software release. This software is not a single, monolithic solver; rather, it is a suite of programs and routines designed to serve as building blocks for constructing complete algorithms.