Constrained convex minimization via model-based excessive gap

We introduce a model-based excessive gap technique to analyze first-order primal- dual methods for constrained convex minimization. As a result, we construct new primal-dual methods with optimal convergence rates on the objective residual and the primal feasibility gap of their iterates separately. Through a dual smoothing and prox-function selection strategy, our framework subsumes the augmented Lagrangian, and alternating methods as special cases, where our rates apply.


Présenté à:
Advances in Neural Information Processing Systems (NIPS) 2014, Montreal, Quebec, Canada, December 8-11, 2014
Année
2014
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 Notice créée le 2014-09-29, modifiée le 2019-03-17

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