4 papers
Randomized Quasi-Gauss--Newton Methods for Solving General Nonlinear Equations
Chengchang Liu, Luo Luo
This paper considers the local convergence for solving general nonlinear equations. We establish randomized quasi-Gauss--Newton methods based on the approximation of the Gram matri…
Stochastic Bilevel Optimization with Heavy-Tailed Noise
Zhuanghua Liu, Luo Luo
This paper considers the smooth bilevel optimization in which the lower-level problem is strongly convex and the upper-level problem is possibly nonconvex. We focus on the stochast…
Incremental Gauss--Newton Methods with Superlinear Convergence Rates
Zhiling Zhou, Zhuanghua Liu, Chengchang Liu +1
This paper addresses the challenge of solving large-scale nonlinear equations with Hölder continuous Jacobians. We introduce a novel Incremental Gauss--Newton (IGN) method within e…
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates
Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
We consider the finite-sum optimization problem, where each component function is strongly convex and has Lipschitz continuous gradient and Hessian. The recently proposed increment…