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Klaus Schittkowski (klaus.schittkowskiunibayreuth.de) Abstract: The Fortran subroutine NLPQLP solves smooth nonlinear programming problems and is an extension of the code NLPQL. The new version is specifically tuned to run under distributed systems. A new 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. Keywords: SQP, sequential quadratic programming, nonlinear programming,numerical algorithm, line search, distributed computing, Fortran code, performance evaluation Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization ) Category 2: Optimization Software and Modeling Systems (Optimization Software Benchmark ) Category 3: Optimization Software and Modeling Systems (Parallel Algorithms ) Citation: Report, Department of Mathematics, University of Bayreuth, 2002 Download: [Postscript][Compressed Postscript][PDF] Entry Submitted: 01/10/2002 Modify/Update this entry  
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