11 citations · 17 across the 10 of their papers we have counts for
10 papers
The Effective Number of Nonzeros: Theory and Regularization for Sparse Recovery
Haoyu He, Hao Wang, Jiashan Wang +1
Classical sparse recovery treats all nonzero entries equally, though numerical noise often creates long tails of negligible coefficients. This paper develops an entropy-based notio…
Constrained Optimization Involving Nonconvex Norms: Optimality Conditions, Algorithm and Convergence
Hao Wang, Yining Gao, Jiashan Wang +1
This paper investigates the optimality conditions for characterizing the local minimizers of the constrained optimization problems involving an norm () of the varia…
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…
Towards An Efficient Approach for the Nonconvex Ball Projection: Algorithm and Analysis
Xiangyu Yang, Jiashan Wang, Hao Wang
This paper primarily focuses on computing the Euclidean projection of a vector onto the ball in which . Such a problem emerges as the core building block in s…
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…
Matrix-Free Solvers for Exact Penalty Subproblems
James V. Burke, Frank E. Curtis, Hao Wang +1
We present two matrix-free methods for approximately solving exact penalty subproblems that arise when solving large-scale optimization problems. The first approach is a novel iter…