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Discrete optimization by relational constraint satisfaction.

J R Ullmann1

  • 1Department of Computer Science, The University, Sheffield, England.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel optimization method for pattern matching problems. It reduces computational demands compared to traditional dynamic programming, offering a more efficient approach to finding optimal vectors.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • Pattern matching frequently involves optimizing objective functions that are sums of component functions.
  • Traditional dynamic programming methods face computational challenges (CPU time, storage) with increasing intervariable interactions.

Purpose of the Study:

  • To develop a more efficient optimization method for pattern matching problems.
  • To address the explosive growth in computational requirements of dynamic programming.

Main Methods:

  • A new approach departs from successive variable elimination in dynamic programming.
  • It establishes constraint relations between interacting variables.
  • Discrete relaxation accelerates a backtrack search to identify all valid vectors.

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Main Results:

  • The proposed method demonstrates reduced computational complexity compared to classical dynamic programming.
  • Experimental comparisons show efficiency gains on pseudorandomly generated objective functions.
  • Optimization is achieved by evaluating the objective function for all constrained vectors.

Conclusions:

  • The novel discrete relaxation and constraint-based method offers a viable alternative to dynamic programming for specific optimization tasks.
  • This approach effectively manages intervariable interactions, leading to improved performance.
  • Further research can explore its application in diverse pattern matching scenarios.