Algorithm 856: APPSPACK 4.0: asynchronous parallel pattern search for derivative-free optimization

APPSPACK is software for solving unconstrained and bound-constrained optimization problems. It implements an asynchronous parallel pattern search method that has been specifically designed for problems characterized by expensive function evaluations. Using APPSPACK to solve optimization problems has several advantages: No derivative information is needed; the procedure for evaluating the objective function can be executed via a separate program or script; the code can be run serially or in parallel, regardless of whether the function evaluation itself is parallel; and the software is freely available. We describe the underlying algorithm, data structures, and features of APPSPACK version 4.0, as well as how to use and customize the software.

[1]  Todd Munson,et al.  The NEOS server for optimization version 4 and beyond. , 2002 .

[2]  Margaret H. Wright,et al.  Direct search methods: Once scorned, now respectable , 1996 .

[3]  Tamara G. Kolda,et al.  On the Convergence of Asynchronous Parallel Pattern Search , 2002, SIAM J. Optim..

[4]  E. Lusk,et al.  Installation guide to mpich, a portable implementation of MPI , 1996 .

[5]  V. Torczon,et al.  Direct search methods: then and now , 2000 .

[6]  Arlen W. Harbaugh,et al.  A modular three-dimensional finite-difference ground-water flow model , 1984 .

[7]  Sandia Report,et al.  Revisiting Asynchronous Parallel Pattern Search for Nonlinear Optimization , 2004 .

[8]  Tamara G. Kolda,et al.  Understanding Asynchronous Parallel Pattern Search , 2003 .

[9]  Marco Sciandrone,et al.  On the Global Convergence of Derivative-Free Methods for Unconstrained Optimization , 2002, SIAM J. Optim..

[10]  YangQuan Chen,et al.  Optimization of a fed-batch fermentation process control competition problem using the NEOS server , 2003 .

[11]  Eric Clarkson,et al.  Experimental determination of object statistics from noisy images. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[12]  Tamara G. Kolda,et al.  Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination , 2004, INFORMS J. Comput..

[13]  William Gropp,et al.  Users guide for mpich, a portable implementation of MPI , 1996 .

[14]  Tamara G. Kolda,et al.  Parallel Optimization of Forging Processes for Optimal Material Properties , 2004 .

[15]  C. T. Kelley,et al.  An Implicit Filtering Algorithm for Optimization of Functions with Many Local Minima , 1995, SIAM J. Optim..

[16]  俞文此.  POSITIVE BASIS AND A CLASS OF DIRECT SEARCH TECHNIQUES , 1979 .

[17]  Tamara G. Kolda,et al.  Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods , 2003, SIAM Rev..

[18]  Robert E. Tarjan,et al.  Self-adjusting binary search trees , 1985, JACM.

[19]  Carl Tim Kelley,et al.  Iterative methods for optimization , 1999, Frontiers in applied mathematics.

[20]  Virginia Torczon,et al.  Pattern Search Methods For Nonlinear Optimization , 1995 .

[21]  V. Torczon,et al.  RANK ORDERING AND POSITIVE BASES IN PATTERN SEARCH ALGORITHMS , 1996 .

[22]  Linda Petzold,et al.  Computational Techniques for Quantification and Optimization of Mixing in Microfluidic Devices , 2022 .

[23]  Owen J. Eslinger,et al.  IFFCO: implicit filtering for constrained optimization, version 2 , 1999 .

[24]  Anthony Skjellum,et al.  A High-Performance, Portable Implementation of the MPI Message Passing Interface Standard , 1996, Parallel Comput..

[25]  Tamara G. Kolda,et al.  Asynchronous parallel pattern search for nonlinear optimization , 2000 .