On relaxation of the Condat-Vũ algorithm for convex-concave saddle point problems

Key Points

• The Condat-Vũ algorithm demonstrates relaxation in handling convex-concave saddle point problems, improving convergence.
• Key evidence shows enhanced convergence rates under relaxed conditions as tested against traditional methods.
• Analysis of the proposed algorithm indicates robust performance under convex-concave frameworks, optimizing computational efficiency.
• This study highlights the potential for broader application of relaxed algorithms in optimization tasks, needing further evaluation.