10 papers · 1 filter
Accelerated Prox-Level Methods for Unknown Piecewise-Smooth Optimization I: Convex Optimization
Zhenwei Lin, Zhe Zhang
We introduce a nearly parameter-free algorithm for minimizing piecewise smooth (PWS) convex functions under the quadratic-growth (QG) condition, where the locations and structure o…
Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization
Qi Deng, Guanghui Lan, Zhenwei Lin
This paper presents new first-order methods for achieving optimal oracle complexities in convex optimization with convex functional constraints. Oracle complexities are measured by…
A Practical GPU-Enhanced Matrix-Free Primal-Dual Method for Large-Scale Conic Programs
Zhenwei Lin, Zikai Xiong, Dongdong Ge +1
In this paper, we introduce a practical GPU-enhanced matrix-free first-order method for solving large-scale conic programming problems, which we refer to as PDCS, standing for the…
A Technical Note on the Implementation and Use of PDCS
Zhenwei Lin, Zikai Xiong, Dongdong Ge +1
This technical note documents the implementation and use of the Primal-Dual Conic Programming Solver (PDCS), a first-order solver for large-scale conic optimization problems introd…
Revisiting Randomized Smoothing: Nonsmooth Nonconvex Optimization Beyond Global Lipschitz Continuity
Jingfan Xia, Zhenwei Lin, Qi Deng
Randomized smoothing is a widely adopted technique for optimizing nonsmooth objective functions. However, its efficiency analysis typically relies on global Lipschitz continuity, a…
Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization
Zhenwei Lin, Jingfan Xia, Qi Deng +1
We consider decentralized gradient-free optimization of minimizing Lipschitz continuous functions that satisfy neither smoothness nor convexity assumption. We propose two novel gra…