3 citations · 3 across the 3 of their papers we have counts for
6 papers
An Extrapolated Iteratively Reweighted l1 Method with Complexity Analysis
Hao Wang, Hao Zeng, Jiashan Wang
The iteratively reweighted l1 algorithm is a widely used method for solving various regularization problems, which generally minimize a differentiable loss function combined with a…
Convergence Rate Analysis of Proximal Iteratively Reweighted Methods for Regularization Problems
Hao Wang, Hao Zeng, Jiashan Wang
In this paper, we focus on the local convergence rate analysis of the proximal iteratively reweighted algorithms for solving regularization problems, which are wi…
Relating lp regularization and reweighted l1 regularization
Hao Wang, Hao Zeng, Jiashan Wang
We propose a general framework of iteratively reweighted l1 methods for solving lp regularization problems. We prove that after some iteration k, the iterates generated by the prop…
Inexact Primal-Dual Gradient Projection Methods for Nonlinear Optimization on Convex Set
Fan Zhang, Hao Wang, Jiashan Wang +1
In this paper, we propose a novel primal-dual inexact gradient projection method for nonlinear optimization problems with convex-set constraint. This method only needs inexact comp…
An Inexact First-order Method for Constrained Nonlinear Optimization
Hao Wang, Fan Zhang, Jiashan Wang +1
The primary focus of this paper is on designing an inexact first-order algorithm for solving constrained nonlinear optimization problems. By controlling the inexactness of the subp…
Inexact Sequential Quadratic Optimization with Penalty Parameter Updates Within the QP Solve: Extended Version
James V. Burke, Frank E. Curtis, Hao Wang +1
This paper focuses on the design of sequential quadratic optimization (commonly known as SQP) methods for solving large-scale nonlinear optimization problems. The most computationa…