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Free-Steering Relaxation Methods for Problems with Strictly Convex Costs and Linear Constraints

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  • K. Kiwiel

Abstract

We consider dual coordinate ascent methods for minimizing a strictly convex (possibly nondifferentiable) function subject to linear constraints. Such methods are useful in large-scale applications (e.g., entropy maximization, quadratic programming, network flows), because they are simple, can exploit sparsity and in certain cases are highly parallelizable. We establish their global convergence under weak conditions and a free-steering order of relaxation. Previous comparable results were restricted to special problems with separable costs and equality constraints. Our convergence framework unifies to a certain extent the approaches of Bregman, Censor and Lent, De Pierro and Iusem, and Luo and Tseng, and complements that of Bertsekas and Tseng.

Suggested Citation

  • K. Kiwiel, 1994. "Free-Steering Relaxation Methods for Problems with Strictly Convex Costs and Linear Constraints," Working Papers wp94089, International Institute for Applied Systems Analysis.
    • Handle: RePEc:wop:iasawp:wp94089
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    1. K. Kiwiel, 1995. "Proximal Minimization Methods with Generalized Bregman Functions," Working Papers wp95024, International Institute for Applied Systems Analysis.

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