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Related Experiment Videos

An Economics Approach to Hard Computational Problems

Huberman1, Lukose, Hogg

  • 1Dynamics of Computation Group, Xerox Palo Alto Research Center, Palo Alto, CA 94304, USA.

Science (New York, N.Y.)
|January 3, 1997
PubMed
Summary

This study introduces a novel computational portfolio design method. It combines algorithms to create superior programs, applicable to diverse problems like DNA sequencing and resource-constrained task completion.

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Area of Science:

  • Computer Science
  • Computational Complexity
  • Algorithm Design

Background:

  • Existing algorithms often have limitations when applied to complex problems.
  • There is a need for methods to systematically improve algorithmic performance by combining existing solutions.

Purpose of the Study:

  • To present a general method for algorithm combination.
  • To develop a computational portfolio design procedure.
  • To demonstrate the superiority of combined algorithms over individual ones.

Main Methods:

  • The method is based on economic principles of risk management.
  • It involves designing computational portfolios by combining algorithms.
  • The approach was tested on a canonical NP-complete problem.

Main Results:

  • The developed method successfully created new programs preferable to component algorithms.
  • The computational portfolio design procedure proved effective.
  • The method demonstrated broad applicability.

Conclusions:

  • The proposed method offers a robust framework for algorithm combination.
  • This approach enhances problem-solving capabilities across various domains.
  • It provides a valuable tool for tackling complex computational challenges.

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