3 citations · 4 across the 2 of their papers we have counts for
2 papers
math.OC2022★ 3 cited
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization
Tianyi Lin, Zeyu Zheng, Michael I. Jordan
Nonsmooth nonconvex optimization problems broadly emerge in machine learning and business decision making, whereas two core challenges impede the development of efficient solution…
math.OC2020★ 1 cited
New Proximal Newton-Type Methods for Convex Optimization
Ilan Adler, Zhiyue Tom Hu, Tianyi Lin
In this paper, we propose new proximal Newton-type methods for convex optimization problems in composite form. The applications include model predictive control (MPC) and embedded…