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conference paper not in proceedings
Constrained convex minimization via model-based excessive gap
2014
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.
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Name
NIPS2014-EG-506_TranDinhCevher.pdf
Type
Publisher's version
Access type
openaccess
Size
441.56 KB
Format
Adobe PDF
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