In mostdirectional algorithms for optimization of a mathematical function, thestep along each direction is chosen to optimize the function along the direction.Multistep procedures, determining several directions and steps along the directions, are more efficient than single-step procedures. This paper shows how dynamic programming can be used for the simultaneous determination of optimal steps for a given set of directions. This leads to accelerated convergence. Computational experience for two-step and three-step procedures is also described.
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Communicated by G. L. Nemhauser
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Turner, W.C., Ghare, P.M. Use of dynamic programming to accelerate convergence of directional optimization algorithms. J Optim Theory Appl 16, 39–47 (1975). https://doi.org/10.1007/BF00935622
- Unconstrained minimization
- dynamic programming
- gradient methods
- descent methods
- functional minimization