5 papers
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…
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 ,…
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 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…