Implementation of a Sequential Quadratic Programming Algorithm for Parallel Computing

K. Schittkowski, Report, Department of Mathematics, University of Bayreuth (2002)
Abstract: The paper introduces a new version of the SQP code NLPQL, which is widely used in commercial and academic institutions to solve smooth nonlinear programming problems. The new version, NLPQLP, is specifically tuned to run under distributed systems. Another input parameter L is introduced for the number of parallel machines, that is the number of function calls to be executed simultaneously. In case of L=1, NLPQLP is identical to NLPQL. Otherwise the line search procedure is modified to allow parallel function calls, which can also be applied for approximating gradients by difference formulae. The mathematical background is outlined, in particular the modification of the line search algorithm to retain convergence under parallel systems. Numerical results show the sensitivity of the new version with respect to the number of parallel machines and the influence of different gradient approximations under uncertainty. The performance evaluation is obtained by more than 300 standard test problems. It must be emphasized that the distributed computation of function values is only simulated throughout the paper. It is up to the user to adopt the code to a particular parallel environment.

To download a preprint, click here: NLPQLP_perf.pdf

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