Proximal regularization for the saddle point gradient dynamics

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Date
2021
Authors
Goldsztajn, Diego
Paganini, Fernando
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Abstract
This paper concerns the solution of a convex optimization problem through the saddle point gradient dynamics. Instead of using the standard Lagrangian as is classical in this method, we consider a regularized Lagrangian obtained through a proximal minimization step.We show that, without assumptions of smoothness or strict convexity in the original problem, the regularized Lagrangian is smooth and leads to globally convergent saddle point dynamics. The method is demonstrated through an application to resource allocation in cloud computing.
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8 p.
Notes
Incluye bibliografía.
Subject
CONVEX OPTIMIZATION, PROXIMAL METHOD, SADDLE POINT DYNAMICS
Type
Preprint
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