# Trust-Region Stochastic Optimization with Variance Reduction Technique

arXiv: https://arxiv.org/abs/2412.00673 · HTML: https://mlsanity.com/paper/2412.00673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Stochastic Trust-Region Methods for Over-parameterized Models](https://mlsanity.com/paper/2604.14017/) (2026)

## Abstract (arXiv)

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.