A Partitioned ε-Relaxation Algorithm for Separable Convex
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Parallel Algorithms for Solving the Convex Minimum Cost Flow Problem
Computational Optimization and Applications
Journal of Optimization Theory and Applications
Minimum-cost multicast over coded packet networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
A discriminative matching approach to word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Structured Prediction, Dual Extragradient and Bregman Projections
The Journal of Machine Learning Research
A proximal subgradient projection algorithm for linearly constrained strictly convex problems
Optimization Methods & Software
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We propose a new method for the solution of the single commodity, separable convex cost network flow problem. The method generalizes the $\epsilon$-relaxation method developed for linear cost problems and reduces to that method when applied to linear cost problems. We show that the method terminates with a near optimal solution, and we provide an associated complexity analysis. We also present computational results showing that the method is much faster than earlier relaxation methods, particularly for ill-conditioned problems.