7 papers
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…
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…
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…
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…
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…
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…