13 citations · 38 across the 17 of their papers we have counts for
20 papers
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…
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…
Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network
Zih-Syuan Huang, Ching-pei Lee
We propose a Regularized Adaptive Momentum Dual Averaging (RAMDA) algorithm for training structured neural networks. Similar to existing regularized adaptive methods, the subproble…
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…
Global convergence and acceleration of projection methods for feasibility problems involving union convex sets
Jan Harold Alcantara, Ching-pei Lee
We prove global convergence of classical projection algorithms for feasibility problems involving union convex sets, which refer to sets expressible as the union of a finite number…
Training Structured Neural Networks Through Manifold Identification and Variance Reduction
Zih-Syuan Huang, Ching-pei Lee
This paper proposes an algorithm (RMDA) for training neural networks (NNs) with a regularization term for promoting desired structures. RMDA does not incur computation additional t…
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…
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization
Ching-pei Lee, Cong Han Lim, Stephen J. Wright
We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving empirical risk minimization (ERM) problems with…
First-order algorithms converge faster than on convex problems
Ching-pei Lee, Stephen J. Wright
It is well known that both gradient descent and stochastic coordinate descent achieve a global convergence rate of in the objective value, when applied to a scheme for min…
Inexact Variable Metric Stochastic Block-Coordinate Descent for Regularized Optimization
Ching-pei Lee, Stephen J. Wright
Block-coordinate descent (BCD) is a popular framework for large-scale regularized optimization problems with block-separable structure. Existing methods have several limitations. T…
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
Ching-pei Lee, Cong Han Lim, Stephen J. Wright
We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving ERM problems with a nonsmooth regularization ter…
Inexact Successive Quadratic Approximation for Regularized Optimization
Ching-pei Lee, Stephen J. Wright
Successive quadratic approximations, or second-order proximal methods, are useful for minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part tha…
Using Neural Networks to Detect Line Outages from PMU Data
Ching-pei Lee, Stephen J. Wright
We propose an approach based on neural networks and the AC power flow equations to identify single- and double-line outages in a power grid using the information from phasor measur…
Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization
Ching-pei Lee, Kai-Wei Chang
In recent years, there is a growing need to train machine learning models on a huge volume of data. Designing efficient distributed optimization algorithms for empirical risk minim…
Analyzing Random Permutations for Cyclic Coordinate Descent
Stephen J. Wright, Ching-Pei Lee
We consider coordinate descent methods on convex quadratic problems, in which exact line searches are performed at each iteration. (This algorithm is identical to Gauss-Seidel on t…
Random Permutations Fix a Worst Case for Cyclic Coordinate Descent
Ching-Pei Lee, Stephen J. Wright
Variants of the coordinate descent approach for minimizing a nonlinear function are distinguished in part by the order in which coordinates are considered for relaxation. Three com…
On the Equivalence of CoCoA+ and DisDCA
Ching-pei Lee
In this document, we show that the algorithm CoCoA+ (Ma et al., ICML, 2015) under the setting used in their experiments, which is also the best setting suggested by the authors tha…
Distributed Training of Structured SVM
Ching-pei Lee, Kai-Wei Chang, Shyam Upadhyay +1
Training structured prediction models is time-consuming. However, most existing approaches only use a single machine, thus, the advantage of computing power and the capacity for la…