Computational Optimization and Applications

, Volume 61, Issue 3, pp 609–634 | Cite as

Path following in the exact penalty method of convex programming

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Abstract

Classical penalty methods solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to \(\infty \), one recovers the constrained solution. In the exact penalty method, squared penalties are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. In practice, the kinks in the penalty and the unknown magnitude of the penalty constant prevent wide application of the exact penalty method in nonlinear programming. In this article, we examine a strategy of path following consistent with the exact penalty method. Instead of performing optimization at a single penalty constant, we trace the solution as a continuous function of the penalty constant. Thus, path following starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. For quadratic programming, the solution path is piecewise linear and takes large jumps from constraint to constraint. For a general convex program, the solution path is piecewise smooth, and path following operates by numerically solving an ordinary differential equation segment by segment. Our diverse applications to (a) projection onto a convex set, (b) nonnegative least squares, (c) quadratically constrained quadratic programming, (d) geometric programming, and (e) semidefinite programming illustrate the mechanics and potential of path following. The final detour to image denoising demonstrates the relevance of path following to regularized estimation in inverse problems. In regularized estimation, one follows the solution path as the penalty constant decreases from a large value.

Keywords

Constrained convex optimization Exact penalty Geometric programming Ordinary differential equation Quadratically constrained quadratic programming Regularization Semidefinite programming 

Mathematics Subject Classification

65K05 90C25 

Notes

Acknowledgments

Research supported in part by National Science Foundatation Grant DMS-1310319 and National Institutes of Health Grants GM53275, MH59490, HG006139 and GM105785.

Supplementary material

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Supplementary material 1 (png 181 KB)

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of StatisticsNorth Carolina State UniversityRaleighUSA
  2. 2.Departments of BiomathematicsHuman Genetics and Statistics, University of CaliforniaLos AngelesUSA

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