3 papers
cs.AI2026
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
Minwei Kong, Chonghe Jiang, Ao Qu +24
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a…
math.OC2026
TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reduction
Yuchen Fang, Xinshou Zheng, Javad Lavaei
We propose a stochastic trust-region method for unconstrained nonconvex optimization that incorporates stochastic variance-reduced gradients (SVRG) to accelerate convergence. Unlik…
math.OC2024
Trust-Region Stochastic Optimization with Variance Reduction Technique
Xinshou Zheng
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…