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Global Optimization Methods

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  • Hamm, Lonnie
  • Brorsen, B. Wade

Abstract

Training a neural network is a difficult optimization problem because of numerous local minimums. Many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global algorithm. This study uses Monte-Carlo simulations to determine the relative efficiency of a local search algorithm to 9 stochastic global algorithms. The computational requirements of the global algorithms are several times higher than the local algorithm and there is little gain in using the global algorithms to train neural networks.

Suggested Citation

  • Hamm, Lonnie & Brorsen, B. Wade, 2002. "Global Optimization Methods," 2002 Annual Meeting, July 28-31, 2002, Long Beach, California 36631, Western Agricultural Economics Association.
    • Handle: RePEc:ags:waealb:36631
    DOI: 10.22004/ag.econ.36631
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    References listed on IDEAS

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    1. Francisco J. Solis & Roger J.-B. Wets, 1981. "Minimization by Random Search Techniques," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 19-30, February.
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    Cited by:

    1. Christopher Boyer & B. Brorsen & Tong Zhang, 2014. "Common-value auction versus posted-price selling: an agent-based model approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(1), pages 129-149, April.
    2. Tong Zhang & B. Brorsen, 2009. "Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets," Computational Economics, Springer;Society for Computational Economics, vol. 34(4), pages 399-417, November.
    3. Laura Palagi, 2019. "Global optimization issues in deep network regression: an overview," Journal of Global Optimization, Springer, vol. 73(2), pages 239-277, February.
    4. Laura Palagi, 2017. "Global Optimization issues in Supervised Learning. An overview," DIAG Technical Reports 2017-11, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    5. Christopher Boyer & B. Brorsen, 2014. "Implications of a Reserve Price in an Agent-Based Common-Value Auction," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 33-51, January.

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