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. (Source:

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 29 articles , 1 standard article )

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  1. Ma, Kaiwen; Sahinidis, Nikolaos V.; Rajagopalan, Sreekanth; Amaran, Satyajith; Bury, Scott J.: Decomposition in derivative-free optimization (2021)
  2. Ahmadvand, M.; Esmaeilbeigi, M.; Kamandi, A.; Yaghoobi, F. M.: A novel hybrid trust region algorithm based on nonmonotone and LOOCV techniques (2019)
  3. Berahas, Albert S.; Byrd, Richard H.; Nocedal, Jorge: Derivative-free optimization of noisy functions via quasi-Newton methods (2019)
  4. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  5. Larson, Jeffrey; Wild, Stefan M.: Asynchronously parallel optimization solver for finding multiple minima (2018)
  6. Liuzzi, G.; Truemper, K.: Parallelized hybrid optimization methods for nonsmooth problems using NOMAD and linesearch (2018)
  7. Gao, Guohua; Vink, Jeroen C.; Chen, Chaohui; El Khamra, Yaakoub; Tarrahi, Mohammadali: Distributed Gauss-Newton optimization method for history matching problems with multiple best matches (2017)
  8. Mondal, Sukanto; Lucet, Yves; Hare, Warren: Optimizing horizontal alignment of roads in a specified corridor (2015)
  9. Audet, C.; Dang, C.-K.; Orban, D.: Efficient use of parallelism in algorithmic parameter optimization applications (2013)
  10. Regis, Rommel G.: An initialization strategy for high-dimensional surrogate-based expensive black-box optimization (2013)
  11. Le Digabel, Sébastien: Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm (2011)
  12. Lee, Herbert K. H.; Gramacy, Robert B.; Linkletter, Crystal; Gray, Genetha A.: Optimization subject to hidden constraints via statistical emulation (2011)
  13. Shuttleworth, Robert R.; Elman, Howard C.; Long, Kevin R.; Templeton, Jeremy A.: Fast solvers for models of ICEO microfluidic flows (2011)
  14. Begin, T.; Baynat, B.; Sourd, F.; Brandwajn, A.: A DFO technique to calibrate queueing models (2010)
  15. Custódio, A. L.; Rocha, H.; Vicente, L. N.: Incorporating minimum Frobenius norm models in direct search (2010)
  16. Griffin, Joshua D.; Kolda, Tamara G.: Nonlinearly constrained optimization using heuristic penalty methods and asynchronous parallel generating set search (2010)
  17. Griffin, Joshua D.; Kolda, Tamara G.: Asynchronous parallel hybrid optimization combining DIRECT and GSS (2010)
  18. Zhang, Hongchao; Conn, Andrew R.; Scheinberg, Katya: A derivative-free algorithm for least-squares minimization (2010)
  19. Moré, Jorge J.; Wild, Stefan M.: Benchmarking derivative-free optimization algorithms (2009)
  20. Audet, Charles; Dennis, J. E. jun.; Le Digabel, Sébastien: Parallel space decomposition of the mesh adaptive direct search algorithm (2008)

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