230 citations · 677 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
Towards Constituting Mathematical Structures for Learning to Optimize
Jialin Liu, Xiaohan Chen, Zhangyang Wang +2
Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A g…
Hyperparameter Tuning is All You Need for LISTA
Xiaohan Chen, Jialin Liu, Zhangyang Wang +1
Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) introduces the concept of unrolling an iterative algorithm and training it like a neural network. It has had great succes…
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
HanQin Cai, Jialin Liu, Wotao Yin
Robust principal component analysis (RPCA) is a critical tool in modern machine learning, which detects outliers in the task of low-rank matrix reconstruction. In this paper, we pr…
Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds
Xiaohan Chen, Jialin Liu, Zhangyang Wang +1
In recent years, unfolding iterative algorithms as neural networks has become an empirical success in solving sparse recovery problems. However, its theoretical understanding is st…
First and Second Order Methods for Online Convolutional Dictionary Learning
Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg +1
Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to d…
Online Convolutional Dictionary Learning
Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg +1
While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem. The single biggest impediment to learni…