5 citations · 12 across the 8 of their papers we have counts for
9 papers
Universal and Parameter-free Gradient Sliding for Composite Optimization
Yan Wu, Yuyuan Ouyang, Zhe Zhang +1
We propose a Parameter-Free Universal Gradient Sliding (PFUGS) algorithm for computing an approximate solution to the convex composite optimization ,…
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…
Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization
Guanghui Lan, Yuyuan Ouyang, Zhe Zhang
We propose novel optimal and parameter-free algorithms for computing an approximate solution with small (projected) gradient norm. Specifically, for computing an approximate soluti…
Solving Convex Smooth Function Constrained Optimization Is Almost As Easy As Unconstrained Optimization
Zhe Zhang, Guanghui Lan
While Nesterov's Accelerated Gradient Descent (AGD) efficiently solves constrained problems when the constraint set $X \subseteq \bbr^n$ is simple and easy to project onto, it rema…
Stochastic Compositional Optimization with Compositional Constraints
Shuoguang Yang, Wei You, Zhe Zhang +1
Stochastic compositional optimization (SCO) has attracted considerable attention because of its broad applicability to important real-world problems. However, existing works on SCO…
Optimal Methods for Convex Risk Averse Distributed Optimization
Guanghui Lan, Zhe Zhang
This paper studies the communication complexity of convex risk-averse optimization over a network. The problem generalizes the well-studied risk-neutral finite-sum distributed opti…
The solution space structure of planted constraint satisfaction problems with growing domains
Wei Xu, Zhe Zhang
Planting a solution into the random RB model, which is a prototype of random constraint satisfaction problem (CSP) with growing domains, can generate very hard satisfiable CSP benc…
Optimal Algorithms for Convex Nested Stochastic Composite Optimization
Zhe Zhang, Guanghui Lan
Recently, convex nested stochastic composite optimization (NSCO) has received considerable attention for its applications in reinforcement learning and risk-averse optimization. Th…
Efficient Algorithms for Distributionally Robust Stochastic Optimization with Discrete Scenario Support
Zhe Zhang, Shabbir Ahmed, Guanghui Lan
Recently, there has been a growing interest in distributionally robust optimization (DRO) as a principled approach to data-driven decision making. In this paper, we consider a dist…