4 papers
Proximal bundle methods for hybrid weakly convex composite optimization problems
Jiaming Liang, Renato D. C. Monteiro, Honghao Zhang
This paper establishes the iteration-complexity of proximal bundle methods for solving hybrid (i.e., a blend of smooth and nonsmooth) weakly convex composite optimization (HWC-CO)…
Multi-cut stochastic approximation methods for solving stochastic convex composite optimization
Jiaming Liang, Renato D. C. Monteiro, Honghao Zhang
This paper considers the stochastic convex composite optimization problem and presents multi-cut stochastic approximation (SA) methods for solving it, whose models in expectation o…
Design-based theory for causal inference
Xin Lu, Wanjia Fu, Hongzi Li +4
Causal inference, as a major research area in statistics and data science, plays a central role across diverse fields such as medicine, economics, education, and the social science…
Parameter-free proximal bundle methods with adaptive stepsizes for hybrid convex composite optimization problems
Renato D. C. Monteiro, Honghao Zhang
This paper develops a parameter-free adaptive proximal bundle method with two important features: 1) adaptive choice of variable prox stepsizes that "closely fits" the instance und…