2.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shuguang Cui5 profiles29 · h 63
- Arnulf Jentzen22 · h 49
- Z. Luo18 · h 89
- Guangxu Zhu5 profiles17 · h 33
- Xiaoguang Han2 profiles15 · h 42
- Hang Li2 profiles13 · h 18
- Tsung-Hui Chang11 · h 45
- Qingjiang Shi3 profiles9 · h 36
- H. Poor2 profiles7 · h 141
- Rui Zhang3 profiles7 · h 137
- Shiyin Li7 · h 21
- Xiaoyang Li2 profiles7 · h 9
- Chinese University of Hong Kong, ShenzhenCN158 papers
- University of MünsterDE21 papers
- Chinese University of Hong KongHK16 papers
- University of Hong KongHK15 papers
- ETH ZurichCH14 papers
- Sun Yat-sen UniversityCN13 papers
- National University of SingaporeSG12 papers
- Peng Cheng LaboratoryCN10 papers
- Princeton UniversityUS10 papers
- Tongji UniversityCN10 papers
- Beijing University of Posts and TelecommunicationsCN9 papers
- Chinese Academy of SciencesCN9 papers
16 papers · 1 filter
A Sparse Corner-Difference MILP for Minimum Square Tiling
Zhuo Yu, Yuan Wang, Zhuo Liu
We study the problem of tiling an square with axis-aligned squares whose side lengths are integers strictly less than , with the objective of minimizing the number o…
Parallel Graver Basis Extraction for Nonlinear Integer Optimization
Wenbo Liu, Akang Wang, Wenguo Yang
The augmentation scheme provides a nontraditional approach to nonlinear integer programming by iteratively refining incumbent solutions along objective-improving directions from th…
Target-based Distributionally Robust Minimum Spanning Tree Problem
Yang Xu, Lianmin Zhang
Due to its broad applications in practice, the minimum spanning tree problem and its all kinds of variations have been studied extensively during the last decades, for which a host…
A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction
Andre Milzarek, Fabian Schaipp, Michael Ulbrich
We develop an implementable stochastic proximal point (SPP) method for a class of weakly convex, composite optimization problems. The proposed stochastic proximal point algorithm i…
On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks
Arnulf Jentzen, Adrian Riekert
In this article we study fully-connected feedforward deep ReLU ANNs with an arbitrarily large number of hidden layers and we prove convergence of the risk of the GD optimization me…
Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks
Zhuoqing Song, Lei Shi, Shi Pu +1
In this paper, we propose two communication efficient decentralized optimization algorithms over a general directed multi-agent network. The first algorithm, termed Compressed Push…
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems
Zhiguo Wang, Jiawei Zhang, Tsung-Hui Chang +2
While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems…
Scalable Deep Reinforcement Learning for Ride-Hailing
Jiekun Feng, Mark Gluzman, J. G. Dai
Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange thousands of cars to meet ride requests throughout the day. We consider a Markov decision process (MDP) model o…
Queueing Network Controls via Deep Reinforcement Learning
J. G. Dai, Mark Gluzman
Novel advanced policy gradient (APG) methods, such as Trust Region policy optimization and Proximal policy optimization (PPO), have become the dominant reinforcement learning algor…
Demand Estimation from Sales Transaction Data -- Practical Extensions
Norbert Remenyi, Xiaodong Luo
In this paper we discuss practical limitations of the standard choice-based demand models used in the literature to estimate demand from sales transaction data. We present modifica…
Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery
Nan Meng, Yun-Bin Zhao
Sparse signal recovery or compressed sensing can be formulated as certain sparse optimization problems. The classic optimization theory indicates that the Newton-like method often…
Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms
Konstantin Usevich, Jianze Li, Pierre Comon
We propose a gradient-based Jacobi algorithm for a class of maximization problems on the unitary group, with a focus on approximate diagonalization of complex matrices and tensors…
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization
Yang Yang, Marius Pesavento, Zhi-Quan Luo +1
In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is…
A Linearly Convergent Doubly Stochastic Gauss-Seidel Algorithm for Solving Linear Equations and A Certain Class of Over-Parameterized Optimization Problems
Meisam Razaviyayn, Mingyi Hong, Navid Reyhanian +1
Consider the classical problem of solving a general linear system of equations . It is well known that the (successively over relaxed) Gauss-Seidel scheme and many of its var…
Multi-UAV Interference Coordination via Joint Trajectory and Power Control
Chao Shen, Tsung-Hui Chang, Jie Gong +2
In this paper, we consider an unmanned aerial vehicle-enabled interference channel (UAV-IC), where each of the UAVs communicates with its associated ground terminals (GTs) at t…
A Study of Piecewise Linear-Quadratic Programs
Ying Cui, Tsung-Hui Chang, Mingyi Hong +1
Motivated by a growing list of nontraditional statistical estimation problems of the piecewise kind, this paper provides a survey of known results supplemented with new results for…