Optimization Strategies in Complex Systems
L. Bussolari, P. Contucci, C. Giardina', C. Giberti, F. Unguendoli, C. Vernia

TL;DR
This paper analyzes the performance of different optimization algorithms in complex combinatorial systems across various scientific domains, focusing on their dynamics and system size effects.
Contribution
It introduces a comparative analysis of greedy, reluctant, and stochastic convex interpolation algorithms in complex systems, highlighting their performance characteristics.
Findings
Greedy algorithm decreases quickly along the gradient.
Reluctant algorithm decreases slowly near level curves.
Stochastic convex interpolation balances the two approaches.
Abstract
We consider a class of combinatorial optimization problems that emerge in a variety of domains among which: condensed matter physics, theory of financial risks, error correcting codes in information transmissions, molecular and protein conformation, image restoration. We show the performances of two algorithms, the``greedy'' (quick decrease along the gradient) and the``reluctant'' (slow decrease close to the level curves) as well as those of a``stochastic convex interpolation''of the two. Concepts like the average relaxation time and the wideness of the attraction basin are analyzed and their system size dependence illustrated.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTheoretical and Computational Physics · Topological and Geometric Data Analysis · Markov Chains and Monte Carlo Methods
