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A first-order primal-dual method for nonconvex constrained optimization based on the augmented Lagrangian

Zhu, Daoli; Zhao, Lei; Zhang, Shuzhong - In: Mathematics of operations research 49 (2024) 1, pp. 125-150

Persistent link: https://www.econbiz.de/10014527298

First-order algorithms without lipschitz gradient : a sequential local optimization approach

Zhang, Junyu; Hong, Mingyi - In: INFORMS journal on optimization 6 (2024) 2, pp. 118-136

Persistent link: https://www.econbiz.de/10014555586

A new Lagrangian-based first-order method for nonconvex constrained optimization

Kim, Jong Gwang - In: Operations research letters 51 (2023) 3, pp. 357-363

Persistent link: https://www.econbiz.de/10014374986

Accelerated stochastic algorithms for convex-concave saddle-point problems

Zhao, Renbo - In: Mathematics of operations research 47 (2022) 2, pp. 1443-1473

Persistent link: https://www.econbiz.de/10013365319

An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization

Lin, Qihang; Xiao, Lin - In: Computational Optimization and Applications 60 (2015) 3, pp. 633-674

<Para ID="Par1">We consider optimization problems with an objective function that is the sum of two convex terms: one is smooth and given by a black-box oracle, and the other is general but with a simple, known structure. We first present an accelerated proximal gradient (APG) method for problems where the...</para>

Persistent link: https://www.econbiz.de/10011241264