3 papers
math.OC2026
Nearly Optimal Risk Minimization
Zhichao Jia, Guanghui Lan, Zhe Zhang
Convex risk measures play a foundational role in the area of stochastic optimization. However, in contrast to risk neutral models, their applications are still limited due to the l…
math.OC2026
Value Mirror Descent for Reinforcement Learning
Zhichao Jia, Guanghui Lan
Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL). Under a generative sampling model, these me…
math.OC2025
First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or without Slater Points
Zhichao Jia, Benjamin Grimmer
Constrained optimization problems where both the objective and constraints may be nonsmooth and nonconvex arise across many learning and data science settings. In this paper, we sh…