dorsal/arxiv
View SchemaThe Scaling Behaviour of Stochastic Minimization Algorithms in a Perfect Funnel Landscape
| Authors | K. Hamacher, W. Wenzel |
|---|---|
| Categories | |
| ArXiv ID | physics/9810035 |
| URL | https://arxiv.org/abs/physics/9810035 |
| DOI | 10.1103/PhysRevE.59.938 |
| Journal | Phys. Rev. E 59, 939 (1999) |
Abstract
We determined scaling laws for the numerical effort to find the optimal configurations of a simple model potential energy surface (PES) with a perfect funnel structure that reflects key characteristics of the protein interactions. Generalized Monte-Carlo methods(MCM, STUN) avoid an enumerative search of the PES and thus provide a natural resolution of the Levinthal paradox. We find that the computational effort grows with approximately the eighth power of the system size for MCM and STUN, while a genetic algorithm was found to scale exponentially. The scaling behaviour of a derived lattice model is also rationalized.
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"abstract": "We determined scaling laws for the numerical effort to find the optimal\nconfigurations of a simple model potential energy surface (PES) with a perfect\nfunnel structure that reflects key characteristics of the protein interactions.\nGeneralized Monte-Carlo methods(MCM, STUN) avoid an enumerative search of the\nPES and thus provide a natural resolution of the Levinthal paradox. We find\nthat the computational effort grows with approximately the eighth power of the\nsystem size for MCM and STUN, while a genetic algorithm was found to scale\nexponentially. The scaling behaviour of a derived lattice model is also\nrationalized.",
"arxiv_id": "physics/9810035",
"authors": [
"K. Hamacher",
"W. Wenzel"
],
"categories": [
"physics.bio-ph"
],
"doi": "10.1103/PhysRevE.59.938",
"journal_ref": "Phys. Rev. E 59, 939 (1999)",
"title": "The Scaling Behaviour of Stochastic Minimization Algorithms in a Perfect Funnel Landscape",
"url": "https://arxiv.org/abs/physics/9810035"
},
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