Constraint consensus methods for finding interior feasible points in second-order cones

Shafiu Jibrin, Anna Weigandt, Kaitlyn Tuthill

Research output: Contribution to journalArticle

Abstract

Optimization problems with second-order cone constraints (SOCs) can be solved efficiently by interior point methods. In order for some of these methods to get started or to converge faster, it is important to have an initial feasible point or near-feasible point. In this paper, we study and apply Chinnecks Original constraint consensus method and DBmax constraint consensus method to find near-feasible points for systems of SOCs. We also develop and implement a new backtracking-like line search technique on these methods that attempts to increase the length of the consensus vector, at each iteration, with the goal of finding interior feasible points. Our numerical results indicate that the new methods are effective in finding interior feasible points for SOCs.

Original languageEnglish (US)
Article number307209
JournalJournal of Applied Mathematics
Volume2010
DOIs
StatePublished - 2010

Fingerprint

Second-order Cone
Cones
Cone Constraints
Interior
Backtracking
Interior Point Method
Line Search
Optimization Problem
Converge
Iteration
Numerical Results

ASJC Scopus subject areas

  • Applied Mathematics

Cite this

Constraint consensus methods for finding interior feasible points in second-order cones. / Jibrin, Shafiu; Weigandt, Anna; Tuthill, Kaitlyn.

In: Journal of Applied Mathematics, Vol. 2010, 307209, 2010.

Research output: Contribution to journalArticle

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