Trust-Region Stochastic Optimization with Variance Reduction Technique
Optimization and Control
2024-12-03 v1
Abstract
We propose a novel algorithm, TR-SVR, for solving unconstrained stochastic optimization problems. This method builds on the trust-region framework, which effectively balances local and global exploration in optimization tasks. TR-SVR incorporates variance reduction techniques to improve both computational efficiency and stability when addressing stochastic objective functions. The algorithm applies a sequential quadratic programming (SQP) approach within the trust-region framework, solving each subproblem approximately using variance-reduced gradient estimators. This integration ensures a robust convergence mechanism while maintaining efficiency, making TR-SVR particularly suitable for large-scale stochastic optimization challenges.
Cite
@article{arxiv.2412.00673,
title = {Trust-Region Stochastic Optimization with Variance Reduction Technique},
author = {Xinshou Zheng},
journal= {arXiv preprint arXiv:2412.00673},
year = {2024}
}