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A Primal-Dual Homotopy Algorithm for l_1-Minimization with l_inf-Constraints

Christoph Brauer (ch.brauer***at***tu-braunschweig.de)
Dirk A. Lorenz (d.lorenz***at***tu-braunschweig.de)
Andreas M. Tillmann (tillmann***at***mathematik.tu-darmstadt.de)

Abstract: In this paper we propose a primal-dual homotopy method for $\ell_1$-minimization problems with infinity norm constraints in the context of sparse reconstruction. The natural homotopy parameter is the value of the bound for the constraints and we show that there exists a piecewise linear solution path with finitely many break points for the primal problem and a respective piecewise constant path for the dual problem. We show that by solving a small linear program, one can jump to the next primal break point and then, solving another small linear program, a new optimal dual solution is calculated which enables the next such jump in the subsequent iteration. Using a theorem of the alternative, we show that the method never gets stuck and indeed calculates the whole path in a finite number of steps. Numerical experiments demonstrate the effectiveness of our algorithm. In many cases, our method significantly outperforms commercial LP solvers; this is possible since our approach employs a sequence of considerably simpler auxiliary linear programs that can be solved efficiently with specialized active-set strategies.

Keywords: Convex Optimization, Dantzig Selector, Homotopy Methods, Nonsmooth Optimization, Primal-Dual Methods

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Category 2: Linear, Cone and Semidefinite Programming (Linear Programming )


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Entry Submitted: 10/31/2016
Entry Accepted: 10/31/2016
Entry Last Modified: 02/17/2018

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