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On the convergence rate of dual ascent methods for linearly constrained convex minimization. (English) Zbl 0804.90103
This paper studied the convergence rate of dual ascent methods for linearly constrained convex minimization problems. Under some conditions it is shown that dual coordinate ascent methods and dual gradient methods attain a linear rate of convergence. This is achieved by estimating the distance from a feasible dual solution to the optimal dual solution set by the norm of a certain residual. The dual ascent methods are particularly useful in solving large-scale optimization problems with separable objective functions.

MSC:
90C25 Convex programming
90C06 Large-scale problems in mathematical programming
90B10 Deterministic network models in operations research
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