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Parallel computing in nonconvex programming

  • Section II Algorithms For Parallel Computers
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Abstract

In this paper, we are concerned with the development of parallel algorithms for solving some classes of nonconvex optimization problems. We present an introductory survey of parallel algorithms that have been used to solve structured problems (partially separable, and large-scale block structured problems), and algorithms based on parallel local searches for solving general nonconvex problems. Indefinite quadratic programming posynomial optimization, and the general global concave minimization problem can be solved using these approaches. In addition, for the minimum concave cost network flow problem, we are going to present new parallel search algorithms for large-scale problems. Computational results of an efficient implementation on a multi-transputer system will be presented.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, 32611, Gainesville, FL, USA

    Panos M. Pardalos

  2. HRB Systems, Science Park Road, 16804, State College, PA, USA

    G. M. Guisewite

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  1. Panos M. Pardalos
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  2. G. M. Guisewite
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Pardalos, P.M., Guisewite, G.M. Parallel computing in nonconvex programming. Ann Oper Res 43, 87–107 (1993). https://doi.org/10.1007/BF02024487

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