7 papers
Accelerating Inexact Successive Quadratic Approximation for Regularized Optimization Through Manifold Identification
Ching-pei Lee
For regularized optimization that minimizes the sum of a smooth term and a regularizer that promotes structured solutions, inexact proximal-Newton-type methods, or successive quadr…
Huge-Scale Assortment Optimization with Customer Choice: A Parallel Primal-Dual Approach
Donghao Zhu, Hanzhang Qin, Ching-pei Lee +3
We study huge-scale assortment optimization problems to maximize expected revenue under customer choice, addressing a fundamental challenge in industries such as transportation, re…
Accelerated projected gradient algorithms for sparsity constrained optimization problems
Jan Harold Alcantara, Ching-pei Lee
We consider the projected gradient algorithm for the nonconvex best subset selection problem that minimizes a given empirical loss function under an -norm constraint. Throu…
Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition
Ching-pei Lee, Ling Liang, Tianyun Tang +1
This work proposes a rapid algorithm, BM-Global, for nuclear-norm-regularized convex and low-rank matrix optimization problems. BM-Global efficiently decreases the objective value…
Revisiting Superlinear Convergence of Proximal Newton-Like Methods to Degenerate Solutions
Ching-pei Lee, Stephen J. Wright
We describe inexact proximal Newton-like methods for solving degenerate regularized optimization problems and for the broader problem of finding a zero of a generalized equation th…
A four-operator splitting algorithm for nonconvex and nonsmooth optimization
Jan Harold Alcantara, Ching-pei Lee, Akiko Takeda
In this work, we address a class of nonconvex nonsmooth optimization problems where the objective function is the sum of two smooth functions (one of which is proximable) and two n…