9 citations · 16 across the 5 of their papers we have counts for
6 papers
Fully discretized Sobolev gradient flow for the Gross-Pitaevskii eigenvalue problem
Ziang Chen, Jianfeng Lu, Yulong Lu +1
This paper studies the numerical approximation of the ground state of the Gross-Pitaevskii (GP) eigenvalue problem with a fully discretized Sobolev gradient flow induced by the $H^…
Exact and Efficient Representation of Totally Anti-Symmetric Functions
Ziang Chen, Jianfeng Lu
This paper concerns the long-standing question of representing (totally) anti-symmetric functions in high dimensions. We propose a new ansatz based on the composition of an odd fun…
On the convergence of Sobolev gradient flow for the Gross-Pitaevskii eigenvalue problem
Ziang Chen, Jianfeng Lu, Yulong Lu +1
We study the convergences of three projected Sobolev gradient flows to the ground state of the Gross-Pitaevskii eigenvalue problem. They are constructed as the gradient flows of th…
Representation Theorem for Multivariable Totally Symmetric Functions
Chongyao Chen, Ziang Chen, Jianfeng Lu
In this work, we establish a representation theorem for multivariable totally symmetric functions: a multisymmetric continuous function must be the composition of a continuous func…
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
Ziang Chen, Jialin Liu, Xinshang Wang +2
While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100--fold speedup in the past twenty years. Still, many classes of MILPs qu…
On Representing Linear Programs by Graph Neural Networks
Ziang Chen, Jialin Liu, Xinshang Wang +2
Learning to optimize is a rapidly growing area that aims to solve optimization problems or improve existing optimization algorithms using machine learning (ML). In particular, the…