Permutation Picture of Graph Combinatorial Optimization Problems

TL;DR

This paper introduces a permutation-based framework for representing various graph combinatorial optimization problems, aiming to facilitate new algorithmic approaches and bridge discrete and continuous optimization methods.

Contribution

It presents a novel permutation picture framework that unifies multiple graph problems and opens new directions for neural combinatorial optimization.

Findings

Framework effectively models diverse graph problems.

Potential to enhance algorithm design in neural optimization.

Bridges discrete and continuous optimization techniques.

Abstract

This paper proposes a framework that formulates a wide range of graph combinatorial optimization problems using permutation-based representations. These problems include the travelling salesman problem, maximum independent set, maximum cut, and various other related problems. This work potentially opens up new avenues for algorithm design in neural combinatorial optimization, bridging the gap between discrete and continuous optimization techniques.