104 citations · 110 across the 10 of their papers we have counts for
10 papers
Automating Reformulation for Parallel ADMM
Kaizhao Sun, Baihao Wu, Kun Yuan +1
Many real-world optimization models contain exploitable sparsity and block structure, but this structure is often obscured in algebraic form, limiting the effectiveness of modern p…
Expressive Power of Implicit Models: Rich Equilibria and Test-Time Scaling
Jialin Liu, Lisang Ding, Stanley Osher +1
Implicit models, an emerging model class, compute outputs by iterating a single parameter block to a fixed point. This architecture realizes an infinite-depth, weight-tied network…
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
HanQin Cai, Chandra Kundu, Jialin Liu +1
Robust matrix completion (RMC) is a widely used machine learning tool that simultaneously tackles two critical issues in low-rank data analysis: missing data entries and extreme ou…
SAttention: Improving Long Sequence Attention with Smoothed Skeleton Sketching
Xue Wang, Tian Zhou, Jianqing Zhu +6
Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models…
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
Ziang Chen, Xiaohan Chen, Jialin Liu +2
Quadratic programming (QP) is the most widely applied category of problems in nonlinear programming. Many applications require real-time/fast solutions, though not necessarily with…
Learning to optimize: A tutorial for continuous and mixed-integer optimization
Xiaohan Chen, Jialin Liu, Wotao Yin
Learning to Optimize (L2O) stands at the intersection of traditional optimization and machine learning, utilizing the capabilities of machine learning to enhance conventional optim…
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
Ziang Chen, Jialin Liu, Xiaohan Chen +2
Graph neural networks (GNNs) have been widely used to predict properties and heuristics of mixed-integer linear programs (MILPs) and hence accelerate MILP solvers. This paper inves…
Efficient Algorithms for Sum-of-Minimum Optimization
Lisang Ding, Ziang Chen, Xinshang Wang +1
In this work, we propose a novel optimization model termed "sum-of-minimum" optimization. This model seeks to minimize the sum or average of objective functions over parame…
DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee
Haoyu Wang, Jialin Liu, Xiaohan Chen +3
Mixed-integer linear programming (MILP) stands as a notable NP-hard problem pivotal to numerous crucial industrial applications. The development of effective algorithms, the tuning…
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Tian Zhou, Ziqing Ma, Xue wang +5
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they e…