Cosm: Collective Switched Motion for Fast and Accurate Sparse Ising Optimization

This repository contains the preprint “Cosm: Collective Switched Motion for Fast and Accurate Sparse Ising Optimization.” The paper introduces Collective Switched Motion (Cosm), a heuristic algorithm for large-scale sparse Ising-type optimization problems. Cosm combines locally interacting continuous circular variables and non-smooth interactions with global coordination rules that facilitate collective dynamics. It employs a time-varying interaction network that switches periodically, along with a type of selective, correlated perturbation. Benchmark experiments on large bounded-degree Gset instances and planted-solution optimization problems demonstrate substantial improvements in time-to-target and solution quality relative to previously reported results.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-23
DOI
https://doi.org/10.5281/zenodo.20359692
Primary Topic
Complex Network Analysis Techniques
Type
preprint
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preprint

Cosm: Collective Switched Motion for Fast and Accurate Sparse Ising Optimization

Nikhil Shukla, Alexander Marakov, Kenneth Zick
Zenodo (CERN European Organization for Nuclear Research)
Complex Network Analysis Techniques
preprint

Cosm: Collective Switched Motion for Fast and Accurate Sparse Ising Optimization

Nikhil Shukla, Alexander Marakov, Kenneth Zick
preprint en

Abstract

This repository contains the preprint “Cosm: Collective Switched Motion for Fast and Accurate Sparse Ising Optimization.” The paper introduces Collective Switched Motion (Cosm), a heuristic algorithm for large-scale sparse Ising-type optimization problems. Cosm combines locally interacting continuous circular variables and non-smooth interactions with global coordination rules that facilitate collective dynamics. It employs a time-varying interaction network that switches periodically, along with a type of selective, correlated perturbation. Benchmark experiments on large bounded-degree Gset instances and planted-solution optimization problems demonstrate substantial improvements in time-to-target and solution quality relative to previously reported results.

Zenodo (CERN European Organization for Nuclear Research)
University of Southern California (US), Northrop Grumman (Germany) (DE), University of Virginia (US)
Complex Network Analysis Techniques
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