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January 29, 2026Journal of Applied Mathematics and Computing

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

Weining Yang Shan Ma Shibei Xue

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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.

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Implication

Observational analysis reveals improved algorithmic performance for saddle point problems, suggesting effective optimization strategies.

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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69a75d75c6e9836116a2783c https://doi.org/10.1007/s12190-025-02757-w

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