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Surrogate Optimization Toolbox

Project description

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pySOT: Surrogate Optimization Toolbox

pySOT is an asynchronous parallel optimization toolbox for global deterministic optimization problems. The main purpose of the toolbox is for optimization of computationally expensive black-box objective functions with continuous and/or integer variables where the number of evaluations is limited. If there are several processors available it may make sense to evaluate the objective function using either asynchronous or synchronous parallel. pySOT uses the event-driven framework for asynchronous optimization strategies POAP ( to provide this functionality.

The toolbox is hosted on GitHub:


pySOT has been downloaded 17,945 times from 2015-June-4 to 2016-Dec-7


Installation instructions are available at:


Several pySOT examples can be found at:


A two-hour short course on how to use pySOT was given at the CMWR 2016 conference in Toronto. The slides and Python notebooks can be downloaded from:

Check out the new C++ implementation of pySOT:

Check out the new pySOT documentation:

pySOT now has support for Python 3.


Q: I can’t find the GUI
A: You need to install PySide

Q: I can’t find the MARS interpolant
A: You need to install py-earth in order to use MARS. More information is available here:

Q: Can I use pySOT with MPI?
A: Yes. You need to install mpi4py in order to use the MPIController in POAP.

Q: I used pySOT for my research and want to cite it
A: There is currently no published paper on pySOT so we recommend citing pySOT like this: D. Eriksson, D. Bindel, and C. Shoemaker. Surrogate Optimization Toolbox (pySOT)., 2015

Q: Is there support for Python 3?
A: Yes, as of version 0.1.31.

Project details

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Filename, size & hash SHA256 hash help File type Python version Upload date
pySOT-0.1.36.tar.gz (663.8 kB) Copy SHA256 hash SHA256 Source None Jul 21, 2017

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