368 citations · 1.4k across the 51 of their papers we have counts for
7 papers · 1 filter
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.…
Adaptive Primal-Dual Stochastic Gradient Method for Expectation-constrained Convex Stochastic Programs
Yonggui Yan, Yangyang Xu
Stochastic gradient methods (SGMs) have been widely used for solving stochastic optimization problems. A majority of existing works assume no constraints or easy-to-project constra…
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…
Momentum-based variance-reduced proximal stochastic gradient method for composite nonconvex stochastic optimization
Yangyang Xu, Yibo Xu
Stochastic gradient methods (SGMs) have been extensively used for solving stochastic problems or large-scale machine learning problems. Recent works employ various techniques to im…
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…