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204. Minimizing the smoothed gap to solve saddle point problems
Invited abstract in session MD-1: Smoothing techniques for nonsmooth optimization, stream Nonsmooth and nonconvex optimization.
Monday, 16:30-18:30Authors (first author is the speaker)
| 1. | Olivier Fercoq
|
| Telecom Paris University |
Abstract
In this work, we minimize the self-centered smoothed gap, a recently introduced optimality measure, in order to solve convex-concave saddle point problems. The self-centered smoothed gap can be computed as the sum of a convex, possibly nonsmooth function and a smooth weakly convex function. Although it is not convex, we propose an algorithm that minimizes this quantity, effectively reducing convex-concave saddle point problems to a minimization problem. Its worst case complexity is comparable to the state of the art, and the algorithm enjoys linear convergence in favorable cases.
Keywords
- Non-smooth optimization
- Complexity and efficiency of algorithms
- First-order optimization
Status: accepted
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