Abstract
We examine efficient computer implementation of one method of deterministic global optimization, the cutting angle method. In this method the objective function is approximated from below with piecewise linear auxiliary functions. The sequence of global minima of these auxiliary functions converges to the global minimum of the objective function. Computing the minima of the auxiliary function is a combinatorial problem, and we show that it can be effectively parallelized. We discuss the improvements made to the serial implementation of the cutting angle method, and ways of distributing computations across multiple processors on parallel and cluster computers.
This research was supported by the Victorian Partnership for Advanced Computing, Australia
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Beliakov, G., Ting, K.M., Murshed, M., Rubinov, A., Bertoli, M. (2003). Efficient Serial and Parallel Implementations of the Cutting Angle Method. In: Di Pillo, G., Murli, A. (eds) High Performance Algorithms and Software for Nonlinear Optimization. Applied Optimization, vol 82. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0241-4_3
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DOI: https://doi.org/10.1007/978-1-4613-0241-4_3
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