3 papers
math.OC2026
Integrated Learning and Robust Optimization
Cheng Tan, Yuchen Mao, Shuming Wang +1
Many operational decisions require solving a linear program whose cost vector is unknown at decision time and must be predicted from contextual information. Because prediction and…
cs.LG2026
Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
Huangyu Xu, Jingqin Yang, Qianqian Xu +1
Sparse optimization is a fundamental challenge in various practical applications. A popular approach to sparse optimization is regularization. However, it may encounter op…
math.OC2026
Robust Out-of-Distribution Stochastic Optimization
Xianyu Li, Huan Xu, Xiaolin Huang +1
Data-driven decision-making under uncertainty typically presumes the collection of historical data from an unknown target probability distribution. However, one may have no access…