Abstract
This paper introduces a new method for the global unconstrained minimization of a differentiable objective function. The method is based on search trajectories, which are defined by a differential equation and exhibit certain similarities to the trajectories of steepest descent. The trajectories depend explicitly on the value of the objective function and aim at attaining a given target level, while rejecting all larger local minima. Convergence to the gloal minimum can be proven for a certain class of functions and appropriate setting of two parameters.
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Communicated by D. G. Luenberger
The author wishes to thank Professor R. P. Brent for making helpful suggestions and acknowledges the financial support of an Australian National University Postgraduate Scholarship.
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Griewank, A.O. Generalized descent for global optimization. J Optim Theory Appl 34, 11–39 (1981). https://doi.org/10.1007/BF00933356
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DOI: https://doi.org/10.1007/BF00933356