Optimization Letters

, Volume 9, Issue 5, pp 819–838 | Cite as

On solving convex optimization problems with linear ascending constraints

Original Paper

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.

Keywords

Convex optimization Linear ascending constraints Dual method Nonlinear optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringUniversity of MinnesotaMinneapolisUSA

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