Gradient Norm Regularization Second-Order Algorithms for Solving Nonconvex-Strongly Concave Minimax Problems

In this paper, we study second-order algorithms for solving nonconvex-strongly concave minimax problems, which have attracted much attention in recent years in many fields, especially in machine learning. We propose a gradient norm regularized trust-region (GRTR) algorithm to solve nonconvex-strongl...

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Vydáno v:	Journal of scientific computing Ročník 105; číslo 2; s. 41
Hlavní autoři:	Wang, Junlin, Xu, Zi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.11.2025 Springer Nature B.V
Témata:	Algorithms Complexity Computational Mathematics and Numerical Analysis Hessian matrices Jacobi matrix method Jacobian matrix Machine learning Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Minimax technique Optimization algorithms Regularization Theoretical Trust-region 90C30 90C47 Second-order algorithms 90C26 Levenberg-Marquardt algorithm Nonconvex-strongly concave minimax problems
ISSN:	0885-7474, 1573-7691
On-line přístup:	Získat plný text
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