On solving convex optimization problems with linear ascending constraints
- First Online:
- Cite this article as:
- Wang, Z. Optim Lett (2015) 9: 819. doi:10.1007/s11590-014-0806-y
- 252 Downloads
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.