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Optimizing searches via rare events.

Andrea Montanari1, Riccardo Zecchina

  • 1Laboratoire de Physique Théorique de l'ENS, 24 rue Lhomond, 75231 Paris cedex 05, France. montanar@lpt.ens.fr

Physical Review Letters
|May 15, 2002
PubMed
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Randomized search algorithms for complex problems have variable performance. Rare events in these stochastic processes determine running times, and random restarts optimize algorithm performance.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Complexity

Background:

  • Randomized search algorithms are widely used for solving hard combinatorial problems.
  • These algorithms often display significant performance variability.
  • Understanding the underlying stochastic processes is crucial for optimization.

Purpose of the Study:

  • To analyze the rare events in out-of-equilibrium stochastic processes of randomized search algorithms.
  • To elucidate how these rare events collectively influence the distribution of algorithm running times.
  • To demonstrate the performance optimization benefits of random restarts in search algorithms.

Main Methods:

  • Study of rare event phenomena in stochastic processes.
  • Analysis of out-of-equilibrium dynamics.

Related Experiment Videos

  • Mathematical modeling of algorithm running times.

    • Main Results:

    • Identification and classification of different types of rare events.
    • Demonstration of the cooperative effect of rare events on running time distributions.
    • Quantification of performance improvements achieved through random restarts.

    Conclusions:

    • Rare events are critical determinants of performance variability in randomized search.
    • Random restarts offer a provable method for optimizing these algorithms.
    • The findings provide insights into the theoretical underpinnings of randomized search algorithm efficiency.