A New Computational Method for the Sparsest Solutions to Systems of Linear Equations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F15%3A00448595" target="_blank" >RIV/67985556:_____/15:00448595 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1137/140968240" target="_blank" >http://dx.doi.org/10.1137/140968240</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1137/140968240" target="_blank" >10.1137/140968240</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A New Computational Method for the Sparsest Solutions to Systems of Linear Equations

Popis výsledku v původním jazyce

The connection between the sparsest solution to an underdetermined system of linear equations and the weighted l(1)-minimization problem is established in this paper. We show that seeking the sparsest solution to a linear system can be transformed to searching for the densest slack variable of the dual problem of weighted l(1)-minimization with all possible choices of nonnegative weights. Motivated by this fact, a new reweighted l(1)-algorithm for the sparsest solutions of linear systems, going beyond the framework of existing sparsity-seeking methods, is proposed in this paper. Unlike existing reweighted l(1)-methods that are based on the weights defined directly in terms of iterates, the new algorithm computes a weight in dual space via certain convex optimization and uses such a weight to locate the sparsest solutions. It turns out that the new algorithm converges to the sparsest solutions of linear systems under some mild conditions that do not require the uniqueness of the sparses

Název v anglickém jazyce

A New Computational Method for the Sparsest Solutions to Systems of Linear Equations

Popis výsledku anglicky

The connection between the sparsest solution to an underdetermined system of linear equations and the weighted l(1)-minimization problem is established in this paper. We show that seeking the sparsest solution to a linear system can be transformed to searching for the densest slack variable of the dual problem of weighted l(1)-minimization with all possible choices of nonnegative weights. Motivated by this fact, a new reweighted l(1)-algorithm for the sparsest solutions of linear systems, going beyond the framework of existing sparsity-seeking methods, is proposed in this paper. Unlike existing reweighted l(1)-methods that are based on the weights defined directly in terms of iterates, the new algorithm computes a weight in dual space via certain convex optimization and uses such a weight to locate the sparsest solutions. It turns out that the new algorithm converges to the sparsest solutions of linear systems under some mild conditions that do not require the uniqueness of the sparses

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

—

Projekt

<a href="/cs/project/GAP201%2F12%2F0671" target="_blank" >GAP201/12/0671: Variační a numerická analýza v nehladké mechanice kontinua</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

SIAM Journal on Optimization

ISSN

1052-6234

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

25

Strana od-do

1110-1134

Kód UT WoS článku

000357406900015

EID výsledku v databázi Scopus

2-s2.0-84940396270