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Blackbox derivative-free optimization with DFO-TR algorithm
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This package provides an implementation of the derivative-free optimization algorithm, DFO, developed by A. Conn, K. Scheinberg, L. Vicente. Using this package, the user can solve a derivative-free blackbox optimization problem with the DFO method as well as five derivative free algorithms from the scipy.optimize library. The scipy algorithms are the Nelder-Mead, Powell, SLSQP, COBYLA and BFGS algorithms.

To run a set of sample problems, the user can call the “” module.

To solve a user-defined problem:

  • Write your blackbox optimization function in a new Python module. For examples of such functions see blackbox_opt/test_funcs/
  • Write a run file with the module you wrote in the previous step imported. You can modify the module and use it as your run file. Note that you should specify the algorithms(s) that you wish to solve your problems with, a starting point and suitable options.
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