TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization

Abstract

This project has completed. The PI considered optimization problems with uncertainty in the data. Specifically, algorithms and analysis methods were developed for several optimization scenarios where the random parameters were coming from some unknown probability distribution. One notable result efficiently calculates the average optimal solution for a combinatorial regret problem and provides new bounds for how for this may be from the worst case. For details, see the final report. Further, the project produced 8 published papers and 4 more under review or preparation. The specifics of these references are included in uploaded final report. This work has help to advance the state-of-the-art in robust optimization.

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Document Details

Document Type
Technical Report
Publication Date
Nov 28, 2016
Accession Number
AD1030295

Entities

People

  • Horst W. Hamacher
  • Marc Goerigk

Organizations

  • University of Kaiserslautern

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Space

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Civil Engineering
  • Computational Complexity
  • Computations
  • Engineering
  • Evolutionary Algorithms
  • Geometric Programming
  • Integer Programming
  • Linear Programming
  • Mathematical Programming
  • Multiobjective Optimization
  • Operations Research
  • Optimization
  • Probability
  • Probability Distributions
  • Systems Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
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