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Constrained convex minimization via model-based excessive gap

2020-01-19 | |  64 |   47 |   0

Abstract

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

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