1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CV2020
A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems
Qingchao Zhang, Xiaojing Ye, Hongcheng Liu +1
Optimization algorithms for solving nonconvex inverse problem have attracted significant interests recently. However, existing methods require the nonconvex regularization to be sm…
math.OC2019★ 1 cited
Regularized Sample Average Approximation for High-Dimensional Stochastic Optimization Under Low-Rankness
Hongcheng Liu, Charles Hernandez, Hung Yi Lee
This paper concerns a high-dimensional stochastic programming problem of minimizing a function of expected cost with a matrix argument. To this problem, one of the most widely appl…
cs.CC2017★ 1 cited
Optimality condition and complexity analysis for linearly-constrained optimization without differentiability on the boundary
Gabriel Haeser, Hongcheng Liu, Yinyu Ye
In this paper we consider the minimization of a continuous function that is potentially not differentiable or not twice differentiable on the boundary of the feasible region. By ex…