38 citations · 77 across the 21 of their papers we have counts for
7 papers · 1 filter
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
Jicong Fan, Lijun Ding, Chengrun Yang +2
The nuclear norm and Schatten- quasi-norm are popular rank proxies in low-rank matrix recovery. However, computing the nuclear norm or Schatten- quasi-norm of a tensor is har…
A Strict Complementarity Approach to Error Bound and Sensitivity of Solution of Conic Programs
Lijun Ding, Madeleine Udell
In this paper, we provide an elementary, geometric, and unified framework to analyze conic programs that we call the strict complementarity approach. This framework allows us to es…
Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle
Lijun Ding, Yuqian Zhang, Yudong Chen
Existing results for low-rank matrix recovery largely focus on quadratic loss, which enjoys favorable properties such as restricted strong convexity/smoothness (RSC/RSM) and well c…
Revisiting Spectral Bundle Methods: Primal-dual (Sub)linear Convergence Rates
Lijun Ding, Benjamin Grimmer
The spectral bundle method proposed by Helmberg and Rendl is well established for solving large-scale semidefinite programs (SDP) thanks to its low per iteration computational comp…
FW: A Frank-Wolfe style algorithm with stronger subproblem oracles
Lijun Ding, Jicong Fan, Madeleine Udell
This paper proposes a new variant of Frank-Wolfe (FW), called FW. Standard FW suffers from slow convergence: iterates often zig-zag as update directions oscillate around extreme…
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Lijun Ding, Yingjie Fei, Qiantong Xu +1
We develop a novel variant of the classical Frank-Wolfe algorithm, which we call spectral Frank-Wolfe, for convex optimization over a spectrahedron. The spectral Frank-Wolfe algori…
On the simplicity and conditioning of low rank semidefinite programs
Lijun Ding, Madeleine Udell
Low rank matrix recovery problems appear widely in statistics, combinatorics, and imaging. One celebrated method for solving these problems is to formulate and solve a semidefinite…