Free-Steering Relaxation Methods for Problems with Strictly Convex Costs and Linear Constraints
AbstractWe 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.
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Bibliographic InfoPaper provided by International Institute for Applied Systems Analysis in its series Working Papers with number wp94089.
Date of creation: Sep 1994
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- Lamond, B. & Stewart, N. F., 1981. "Bregman's balancing method," Transportation Research Part B: Methodological, Elsevier, vol. 15(4), pages 239-248, August.
- K. Kiwiel, 1995. "Proximal Minimization Methods with Generalized Bregman Functions," Working Papers wp95024, International Institute for Applied Systems Analysis.
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