Unifying Variational View of Accelerated Primal-Dual Methods

Cheng Chang; Mehran Mesbahi

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arXiv · 2609.09454

Unifying Variational View of Accelerated Primal-Dual Methods

Cheng Chang·Mehran Mesbahi

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Abstract

We develop a unifying variational framework for accelerated primal-dual flows in affinely constrained convex optimization. In particular, it is shown that applying the Euler-Lagrange-Rayleigh (ELR) principle to coupled augmented Bregman Lagrangians systematically generates accelerated dynamics over general Bregman geometries and recovers a family of existing primal-dual mirror and Alternating Direction Method of Multipliers (ADMM) flows as special cases. We then establish $\mathcal{O}(e^{-b_t})$ convergence guarantees for these flows under mild assumptions. Lastly, we show that the proposed framework for algorithmic development extends from finite-dimensional optimization to constrained optimization over probability distributions.

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Cheng Chang, Mehran Mesbahi. 2026-09-08. Unifying Variational View of Accelerated Primal-Dual Methods. https://arxiv.org/abs/2609.09454

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