Smoothed Proximal Lagrangian Method for Nonlinear Constrained Programs

Fuente: arXiv
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Autori principali: Pu, Wenqiang, Sun, Kaizhao, Zhang, Jiawei
Natura: Preprint
Pubblicazione: 2024
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author Pu, Wenqiang
Sun, Kaizhao
Zhang, Jiawei
author_facet Pu, Wenqiang
Sun, Kaizhao
Zhang, Jiawei
contents This paper introduces a smoothed proximal Lagrangian method for minimizing a nonconvex smooth function over a convex domain with additional explicit convex nonlinear constraints. Two key features are 1) the proposed method is single-looped, and 2) an first-order iteration complexity of $\mathcal{O}(ε^{-2})$ is established under mild regularity assumptions. The first feature suggests the practical efficiency of the proposed method, while the second feature highlights its theoretical superiority. Numerical experiments on various problem scales demonstrate the advantages of the proposed method in terms of speed and solution quality.
format Preprint
id arxiv_https___arxiv_org_abs_2408_15047
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Smoothed Proximal Lagrangian Method for Nonlinear Constrained Programs
Pu, Wenqiang
Sun, Kaizhao
Zhang, Jiawei
Optimization and Control
This paper introduces a smoothed proximal Lagrangian method for minimizing a nonconvex smooth function over a convex domain with additional explicit convex nonlinear constraints. Two key features are 1) the proposed method is single-looped, and 2) an first-order iteration complexity of $\mathcal{O}(ε^{-2})$ is established under mild regularity assumptions. The first feature suggests the practical efficiency of the proposed method, while the second feature highlights its theoretical superiority. Numerical experiments on various problem scales demonstrate the advantages of the proposed method in terms of speed and solution quality.
title Smoothed Proximal Lagrangian Method for Nonlinear Constrained Programs
topic Optimization and Control
url https://arxiv.org/abs/2408.15047