Local-Minima-Preserving Continuous Relaxation of Ising Problems

Graph neural networks and the energetic cavity method for combinatorial optimization

arXiv:2609.07456v1 Announce Type: cross Abstract: We study the use of graph neural networks (GNNs) for finding approximate ground states of Ising models. Efficiently finding these ground states is of...

Beyond Gradient Descent: Adam for Analog Ising Machines

arXiv:2606. 03917v1 Announce Type: cross Abstract: As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems.

Regularized Large Neighborhood Search

arXiv:2606. 02294v1 Announce Type: new Abstract: Operations research practitioners typically tackle NP-hard combinatorial problems using large neighborhood search (LNS), a scalable heuristic that iteratively refines a current solution by locally re-optimizing subsets of its variables.

Network Learning with Semi-relaxed Gromov-Wasserstein

arXiv:2606. 02223v1 Announce Type: new Abstract: Estimating the generative mechanism of large-scale networks is a fundamental challenge in statistical machine learning.

Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem

Exact Spin Elimination for Quadratic and k-Local Ising Optimization

arXiv:2505.07163v2 Announce Type: replace-cross Abstract: Ising solvers have a finite spin budget. Quadratization uses auxiliary spins to replace higher-order interactions by pairwise ones. We show t...