46 citations · 55 across the 20 of their papers we have counts for
24 papers
Congruence Decomposition with Neural Block Solvers for Large-Scale PCI Assignment
Yeqing Qiu, Chengpiao Huang, Ye Xue +7
Physical Cell Identity (PCI) assignment is essential for interference management in dense 5G networks. As cellular networks scale, PCI reuse becomes unavoidable, which may cause co…
[CLS] is Not Enough: Multi-Label Recognition via Patch-Level Inference and Adaptive Aggregation
Akang Wang, Xili Deng, Zhanxuan Hu +3
Vision-Language Models such as CLIP exhibit strong zero-shot recognition capability by aligning images with textual concepts, yet they often underperform on multi-label recognition…
Promoting Fair Online Resource Allocation with Indivisible Units
Igor Averbakh, Hongyi Jiang, Samitha Samaranayake +2
Allocating scarce, indivisible resources to diverse groups under uncertainty is a central challenge in operations research, where efficiency-focused methods often underserve margin…
Successive Fixing for Large-Scale SCUC Using First-Order Methods
Jinxin Xiong, Yanting Huang, Yingxiao Wang +4
Security-Constrained Unit Commitment is a fundamental optimization problem in power systems operations. The primary computational bottleneck arises from the need to solve large-sca…
An Accelerated Mixed Weighted-Unweighted MMSE Approach for MU-MIMO Beamforming
Xi Gao, Akang Wang, Junkai Zhang +2
Precoding design based on weighted sum-rate (WSR) maximization is a fundamental problem in downlink multi-user multiple-input multiple-output (MU-MIMO) systems. While the weighted…
QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networks
Qian Chen, Linxin Yang, Akang Wang +2
The combination of linear transformations and non-linear activation functions forms the foundation of most modern deep neural networks, enabling them to approximate highly complex…
Smoothing Binary Optimization: A Primal-Dual Perspective
Wenbo Liu, Akang Wang, Dun Ma +3
Binary optimization is a powerful tool for modeling combinatorial problems, yet scalable and theoretically sound solution methods remain elusive. Conventional solvers often rely on…
Solving Quadratic Programs via Deep Unrolled Douglas-Rachford Splitting
Jinxin Xiong, Xi Gao, Linxin Yang +3
Convex quadratic programs (QPs) are fundamental to numerous applications, including finance, engineering, and energy systems. Among the various methods for solving them, the Dougla…
Relax-and-Cut for Temporal SCUC Decomposition
Jinxin Xiong, Linxin Yang, Yingxiao Wang +4
The Security-Constrained Unit Commitment (SCUC) problem presents formidable computational challenges due to its combinatorial complexity, large-scale network dimensions, and numero…
A Learning-Based Inexact ADMM for Solving Quadratic Programs
Xi Gao, Jinxin Xiong, Linxin Yang +3
Convex quadratic programs (QPs) constitute a fundamental computational primitive across diverse domains including financial optimization, control systems, and machine learning. The…
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
Qian Chen, Lei Li, Qian Li +6
A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emer…
Mixed-Integer Linear Optimization via Learning-Based Two-Layer Large Neighborhood Search
Wenbo Liu, Akang Wang, Wenguo Yang +1
Mixed-integer linear programs (MILPs) are extensively used to model practical problems such as planning and scheduling. A prominent method for solving MILPs is large neighborhood s…
An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling
Linxin Yang, Bingheng Li, Tian Ding +6
Quadratic programs (QPs) arise in various domains such as machine learning, finance, and control. Recently, learning-enhanced primal-dual hybrid gradient (PDHG) methods have shown…
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…
ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-k-Cut Problems
Yeqing Qiu, Ye Xue, Akang Wang +3
The Max-k-Cut problem is a fundamental combinatorial optimization challenge that generalizes the classic NP-complete Max-Cut problem. While relaxation techniques are commonly emplo…
On Representing Convex Quadratically Constrained Quadratic Programs via Graph Neural Networks
Chenyang Wu, Qian Chen, Akang Wang +4
Convex quadratically constrained quadratic programs (QCQPs) involve finding a solution within a convex feasible region defined by quadratic constraints while minimizing a convex qu…
IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs
Xi Gao, Jinxin Xiong, Akang Wang +3
Solving constrained nonlinear programs (NLPs) is of great importance in various domains such as power systems, robotics, and wireless communication networks. One widely used approa…
SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization
Qian Chen, Tianjian Zhang, Linxin Yang +5
Integer linear programs (ILPs) are commonly employed to model diverse practical problems such as scheduling and planning. Recently, machine learning techniques have been utilized t…
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
Bingheng Li, Linxin Yang, Yupeng Chen +8
Solving large-scale linear programming (LP) problems is an important task in various areas such as communication networks, power systems, finance and logistics. Recently, two disti…
A Novel Mixed-Integer Linear Programming Formulation for Continuous-Time Inventory Routing
Akang Wang, Xiandong Li, Jeffrey E. Arbogast +2
Inventory management, vehicle routing, and delivery scheduling decisions are simultaneously considered in the context of the inventory routing problem. This paper focuses on the co…
A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming
Qingyu Han, Linxin Yang, Qian Chen +5
Mixed-integer linear programming (MILP) is widely employed for modeling combinatorial optimization problems. In practice, similar MILP instances with only coefficient variations ar…
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse, Quentin Cappart, Jonas Charfreitag +38
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…
Efficient primal heuristics for mixed-integer linear programs
Akang Wang, Linxin Yang, Sha Lai +7
This paper is a short report about our work for the primal task in the Machine Learning for Combinatorial Optimization NeurIPS 2021 Competition. For each dataset of our interest in…
A Scenario Decomposition Algorithm for Strategic Time Window Assignment Vehicle Routing Problems
Anirudh Subramanyam, Akang Wang, Chrysanthos E. Gounaris
We study the strategic decision-making problem of assigning time windows to customers in the context of vehicle routing applications that are affected by operational uncertainty. T…