On solving convex optimization problems with linear ascending constraints

Zizhuo Wang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In this paper, we propose two algorithms for solving convex optimization problems with linear ascending constraints. When the objective function is separable, we propose a dual method which terminates in a finite number of iterations. In particular, the worst case complexity of our dual method improves over the best-known result for this problem in Padakandla and Sundaresan (SIAM J Optim 20(3):1185–1204, 2009). We then propose a gradient projection method to solve a more general class of problems in which the objective function is not necessarily separable. Numerical experiments show that both our algorithms work well in test problems.

Original languageEnglish (US)
Pages (from-to)819-838
Number of pages20
JournalOptimization Letters
Volume9
Issue number5
DOIs
StatePublished - Jun 26 2015

Bibliographical note

Publisher Copyright:
© 2014, Springer-Verlag Berlin Heidelberg.

Keywords

  • Convex optimization
  • Dual method
  • Linear ascending constraints
  • Nonlinear optimization

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