14 citations · 25 across the 5 of their papers we have counts for
10 papers · 1 filter
A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems
Peipei Tang, Chengjing Wang, Bo Jiang
In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex tuning-free robust regression problems. The basic idea is to apply the prox…
An Adaptive High Order Method for Finding Third-Order Critical Points of Nonconvex Optimization
Xihua Zhu, Jiangze Han, Bo Jiang
It is well known that finding a global optimum is extremely challenging for nonconvex optimization. There are some recent efforts \cite{anandkumar2016efficient, cartis2018second, c…
An exact penalty approach for optimization with nonnegative orthogonality constraints
Bo Jiang, Xiang Meng, Zaiwen Wen +1
Optimization with nonnegative orthogonality constraints has wide applications in machine learning and data sciences. It is NP-hard due to some combinatorial properties of the const…
An Optimal High-Order Tensor Method for Convex Optimization
Bo Jiang, Haoyue Wang, Shuzhong Zhang
This paper is concerned with finding an optimal algorithm for minimizing a composite convex objective function. The basic setting is that the objective is the sum of two convex fun…
A Unified Adaptive Tensor Approximation Scheme to Accelerate Composite Convex Optimization
Bo Jiang, Tianyi Lin, Shuzhong Zhang
In this paper, we propose a unified two-phase scheme to accelerate any high-order regularized tensor approximation approach on the smooth part of a composite convex optimization mo…
Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints
Jiang Hu, Bo Jiang, Lin Lin +2
In this paper, we study structured quasi-Newton methods for optimization problems with orthogonality constraints. Note that the Riemannian Hessian of the objective function require…