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Parallel Algorithm for Unconstrained Optimization Based on Decomposition Techniques

Author

Listed:
  • D. Conforti

    (Universitá delta Calabria)

  • R. Musmanno

    (Universitá delta Calabria)

Abstract

We present a numerical implementation of the parallel gradient distribution (PGD) method for the solution of large-scale unconstrained optimization problems. The proposed parallel algorithm is characterized by a parallel phase which exploits the portions of the gradient of the objective function assigned to each processor; then, a coordination phase follows which, by a synchronous interaction scheme, optimizes over the partial results obtained by the parallel phase. The parallel and coordination phases are implemented using a quasi-Newton limited-memory BFGS approach. The computational experiments, carried out on a network of UNIX workstations by using the parallel software tool PVM, show that parallelization efficiency was problem dependent and ranged between 0.15 and 8.75. For the 150 problems solved by PGD on more than one processor, 85 cases had parallelization efficiency below 1, while 65 cases had a parallelization efficiency above 1.

Suggested Citation

  • D. Conforti & R. Musmanno, 1997. "Parallel Algorithm for Unconstrained Optimization Based on Decomposition Techniques," Journal of Optimization Theory and Applications, Springer, vol. 95(3), pages 531-544, December.
    • Handle: RePEc:spr:joptap:v:95:y:1997:i:3:d:10.1023_a:1022665620666
    DOI: 10.1023/A:1022665620666
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    Cited by:

    1. J. Koko, 2013. "Parallel Uzawa Method for Large-Scale Minimization of Partially Separable Functions," Journal of Optimization Theory and Applications, Springer, vol. 158(1), pages 172-187, July.

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