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Surrogate Optimization Toolbox

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pySOT: Python Surrogate Optimization Toolbox

The Python Surrogate Optimization Toolbox (pySOT) is an asynchronous parallel optimization toolbox for computationally expensive global optimization problems. pySOT is built on top of the Plumbing for Optimization with Asynchronous Parallelism (POAP), which is an event-driven framework for building and combining asynchronous optimization strategies. POAP has support for both threads and MPI.

pySOT implements many popular surrogate optimization algorithms such as the Stochastic RBF (SRBF) and DYCORS methods by Regis and Shoemaker, and the SOP method by Krityakierne et. al. We also support Expected Improvement (EI) and Lower Confidence Bounds (LCB), which are popular in Bayesian optimization. All optimization algorithms can be used in serial, synchronous parallel, and asynchronous parallel and we support both continuous and integer variables.

The toolbox is hosted on GitHub:



Installation instructions are available at:


Several pySOT examples and notebooks can be found at:

Citing Us

If you use pySOT, please cite the following paper: David Eriksson, David Bindel, Christine A. Shoemaker. pySOT and POAP: An event-driven asynchronous framework for surrogate optimization. arXiv preprint arXiv:1908.00420, 2019

  title={pySOT and POAP: An event-driven asynchronous framework for surrogate optimization},
  author={Eriksson, David and Bindel, David and Shoemaker, Christine A},
  journal={arXiv preprint arXiv:1908.00420},


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: Please cite our preprint which is currently under review!

Q: Is there support for Python 2?
A: Python 2 support was removed in version 0.2.0

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:

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