5 papers · 1 filter
A Stochastic Proximal-Gradient Method for Regularized Generally Constrained Continuous Optimization
Frank E. Curtis, Xiaoyi Qu, Daniel P. Robinson
A stochastic proximal-gradient method is proposed and analyzed for solving regularized optimization problems subject to nonlinear equality constraints and nonnegativity bounds. The…
Low-Order Explicit Hessian Imitation Method for Large-Scale Supervised Machine Learning
Yunlang Zhu, Lingjun Guo, Zahra Khatti +4
An algorithm is proposed for solving optimization problems arising in neural network training for supervised learning. The unique feature of the algorithm is the use of an auxiliar…
StableQAT: Stable Quantization-Aware Training at Ultra-Low Bitwidths
Tianyi Chen, Sihan Chen, Xiaoyi Qu +5
Quantization-aware training (QAT) is essential for deploying large models under strict memory and latency constraints, yet achieving stable and robust optimization at ultra-low bit…
PRIME: Policy-Reinforced Iterative Multi-agent Execution for Algorithmic Reasoning in Large Language Models
Jiawei Xu, Zhenyu Yu, Ziqian Bi +3
Large language models have demonstrated remarkable capabilities across diverse reasoning tasks, yet their performance on algorithmic reasoning remains limited. To handle this limit…
A Proximal-Gradient Method for Solving Regularized Optimization Problems with General Constraints
Frank E. Curtis, Xiaoyi Qu, Daniel P. Robinson
We propose, analyze, and test a proximal-gradient method for solving regularized optimization problems with general constraints. The method employs a decomposition strategy to comp…