A PrimalDual Homotopy Algorithm for $\ell_{1}$Minimization with $\ell_{\infty}$Constraints
Abstract
In this paper we propose a primaldual 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 activeset strategies.
 Publication:

arXiv eprints
 Pub Date:
 October 2016
 arXiv:
 arXiv:1610.10022
 Bibcode:
 2016arXiv161010022B
 Keywords:

 Mathematics  Optimization and Control;
 Computer Science  Numerical Analysis;
 Mathematics  Numerical Analysis;
 90C05;
 90C255;
 65K05