Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization
Core Concepts
DGDA method introduces a dissipation term to stabilize oscillatory behavior in GDA, achieving superior convergence rates.
Abstract
The content introduces the Dissipative Gradient Descent Ascent (DGDA) method as a solution to unstable oscillations in min-max optimization problems. It incorporates a dissipation term into GDA updates to dampen oscillations and stabilize the system. Theoretical analysis shows linear convergence of DGDA in bilinear and strongly convex-strongly concave settings, outperforming other methods like GDA, Extra-Gradient (EG), and Optimistic GDA. Numerical examples support the effectiveness of DGDA in solving saddle point problems.
I. Introduction:
Focus on solving saddle point problems with considerable attention in various fields.
Standard GDA leads to instability due to oscillatory behavior.
II. Problem Formulation:
Define saddle points for convex-concave functions.
Consider strongly convex-strongly concave and bilinear functions.
III. Dissipative Gradient Descent Ascent Algorithm:
Introduce DGDA as a discretization of a regularization framework for continuous saddle flow dynamics.
Incorporate friction term to dissipate internal energy and stabilize system.
IV. Convergence Analysis:
Linear convergence of DGDA established for bilinear and strongly convex-strongly concave functions.
Superiority of DGDA's convergence rate compared to EG and OGDA methods shown theoretically.
V. Numerical Experiments:
Comparison of performance between DGDA, EG, OGDA, and GDA on bilinear and strongly convex-strongly concave problems.
VI. Conclusion and Future Work:
DGDA method inspired by control theory effectively stabilizes oscillatory behavior in min-max optimization problems.
Dissipative Gradient Descent Ascent Method
Stats
DGDA method achieves better linear convergence rates than other methods such as GDA, EG, OGDA.
Quotes
"By introducing a friction term, the proposed DGDA algorithm dissipates the stored internal energy."
"Our findings demonstrate that DGDA surpasses these methods, achieving superior convergence rates."