38 citations · 77 across the 21 of their papers we have counts for
21 papers
On the Complexity of BFGS Method for Smooth Convex Optimization
Lijun Ding, Jinwen Yang, Baoyu Zhou
We study the BFGS method with an Armijo-Wolfe line search for minimizing convex functions with Lipschitz-continuous gradients, without assuming strong convexity. We establish a glo…
An Overview and Comparison of Spectral Bundle Methods for Primal and Dual Semidefinite Programs
Feng-Yi Liao, Lijun Ding, Yang Zheng
The spectral bundle method developed by Helmberg and Rendl is well-established for solving large-scale semidefinite programs (SDPs) in the dual form, especially when the SDPs admit…
Sharpness and well-conditioning of nonsmooth convex formulations in statistical signal recovery
Lijun Ding, Alex L. Wang
We study a sample complexity vs. conditioning tradeoff in modern signal recovery problems (including sparse recovery, low-rank matrix sensing, covariance estimation, and abstract p…
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
Jun Song, Niao He, Lijun Ding +1
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning. In this paper, we exploit more flexible m…
A Validation Approach to Over-parameterized Matrix and Image Recovery
Lijun Ding, Zhen Qin, Liwei Jiang +2
This paper studies the problem of recovering a low-rank matrix from several noisy random linear measurements. We consider the setting where the rank of the ground-truth matrix is u…
Flat minima generalize for low-rank matrix recovery
Lijun Ding, Dmitriy Drusvyatskiy, Maryam Fazel +1
Empirical evidence suggests that for a variety of overparameterized nonlinear models, most notably in neural network training, the growth of the loss around a minimizer strongly im…