6 papers · 1 filter
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…
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…
Feature Augmentation of GNNs for ILPs: Local Uniqueness Suffices
Qingyu Han, Qian Li, Linxin Yang +3
Integer Linear Programs (ILPs) are central to real-world optimizations but notoriously difficult to solve. Learning to Optimize (L2O) has emerged as a promising paradigm, with Grap…
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…