4 papers
Alternating Stochastic Variance-Reduced Algorithms with Optimal Complexity for Bilevel Optimization
Haimei Huo, Zhixun Su
This paper studies the unconstrained nonconvex-strongly-convex bilevel optimization problem. A common approach to solving this problem is to alternately update the upper-level and…
A Perturbed Value-Function-Based Interior-Point Method for Perturbed Pessimistic Bilevel Problems
Haimei Huo, Risheng Liu, Zhixun Su
Bilevel optimizaiton serves as a powerful tool for many machine learning applications. Perturbed pessimistic bilevel problem PBP, with being an arbitrary positive number, is…
A New Simple Stochastic Gradient Descent Type Algorithm With Lower Computational Complexity for Bilevel Optimization
Haimei Huo, Risheng Liu, Zhixun Su
Bilevel optimization has been widely used in many machine learning applications such as hyperparameter optimization and meta learning. Recently, many simple stochastic gradient des…
Homotopy Methods for Eigenvector-Dependent Nonlinear Eigenvalue Problems
Xuping Zhang, Haimei Huo
Eigenvector-dependent nonlinear eigenvalue problems are considered which arise from the finite difference discretizations of the Gross-Pitaevskii equation. Existence and uniqueness…