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Second-variation methods in dynamic optimization

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Abstract

An entire class of rapid-convergence algorithms, called second-variation methods, is developed for the solution of dynamic optimization problems. Several well-known numerical optimization techniques included in this class are developed from a unified point of view. The generalized Riccati transformation can be applied in conjunction with any second-variation method. This fact is demonstrated for the Newton-Raphson or quasilinearization technique.

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Authors and Affiliations

  1. Kaiser Aluminum and Chemical Corporation, Oakland, California

    A. G. Longmuir (Process Control Engineer)

  2. Department of Electrical Engineering, University of British Columbia, Vancouver, Canada

    E. V. Bohn (Professor)

Authors
  1. A. G. Longmuir
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  2. E. V. Bohn
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Additional information

Communicated by J. V. Breakwell

This work was supported by the National Research Council of Canada under Grant No. 67-3134 and is based on investigations described in more detail in Ref. 1.

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Longmuir, A.G., Bohn, E.V. Second-variation methods in dynamic optimization. J Optim Theory Appl 3, 164–173 (1969). https://doi.org/10.1007/BF00929441

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  • DOI: https://doi.org/10.1007/BF00929441

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