Projective method of multipliers for linearly constrained convex minimization

Majela Pentón Machado ()
Additional contact information
Majela Pentón Machado: UFBA, Universidade Federal da Bahia

Computational Optimization and Applications, 2019, vol. 73, issue 1, No 8, 237-273

Abstract: Abstract We present a method for solving linearly constrained convex optimization problems, which is based on the application of known algorithms for finding zeros of the sum of two monotone operators (presented by Eckstein and Svaiter) to the dual problem. We establish convergence rates for the new method, and we present applications to TV denoising and compressed sensing problems.

Keywords: Constrained optimization; Convex programming; Complexity; Total variation denoising; Compressed sensing; 49M29; 90C25; 65K05; 68Q25 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10589-019-00065-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:coopap:v:73:y:2019:i:1:d:10.1007_s10589-019-00065-1

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-019-00065-1

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().