Interpolating Greedy and Reluctant Algorithms

TL;DR

This paper introduces an interpolating algorithm that combines greedy and reluctant dynamics to optimize NP-complete problems, significantly improving performance over pure strategies within fixed computational time.

Contribution

It proposes a novel interpolation method between greedy and reluctant algorithms, enhancing optimization efficiency for NP-complete problems.

Findings

Optimal interpolation parameter improves performance

Interpolated algorithm outperforms pure strategies

Significant gains within fixed computational time

Abstract

In a standard NP-complete optimization problem we introduce an interpolating algorithm between the quick decrease along the gradient (greedy dynamics) and a slow decrease close to the level curves (reluctant dynamics). We find that for a fixed elapsed computer time the best performance of the optimization is reached at a special value of the interpolation parameter, considerably improving the results of the pure cases greedy and reluctant.