18 citations · 21 across the 3 of their papers we have counts for
11 papers · 1 filter
Inexact accelerated proximal gradient method with line search and reduced complexity for affine-constrained and bilinear saddle-point structured convex problems
Qihang Lin, Yangyang Xu
The goal of this paper is to reduce the total complexity of gradient-based methods for two classes of problems: affine-constrained composite convex optimization and bilinear saddle…
Greedy coordinate descent method on non-negative quadratic programming
Chenyu Wu, Yangyang Xu
The coordinate descent (CD) method has recently become popular for solving very large-scale problems, partly due to its simple update, low memory requirement, and fast convergence.…
First-order methods for problems with O(1) functional constraints can have almost the same convergence rate as for unconstrained problems
Yangyang Xu
First-order methods (FOMs) have recently been applied and analyzed for solving problems with complicated functional constraints. Existing works show that FOMs for functional constr…
Rate-improved Inexact Augmented Lagrangian Method for Constrained Nonconvex Optimization
Zichong Li, Pin-Yu Chen, Sijia Liu +2
First-order methods have been studied for nonlinear constrained optimization within the framework of the augmented Lagrangian method (ALM) or penalty method. We propose an improved…
Augmented Lagrangian based first-order methods for convex-constrained programs with weakly-convex objective
Zichong Li, Yangyang Xu
First-order methods (FOMs) have been widely used for solving large-scale problems. A majority of existing works focus on problems without constraint or with simple constraints. Sev…
Katyusha Acceleration for Convex Finite-Sum Compositional Optimization
Yibo Xu, Yangyang Xu
Structured problems arise in many applications. To solve these problems, it is important to leverage the structure information. This paper focuses on convex problems with a finite-…