optimizers.rst

Surrogate Optimizers

When initializing a new MOOP object (see :doc:`MOOP Classes <class_api>`), you must provide a surrogate optimization problem solver, which will be used to generate candidate solutions for each iteration.

from parmoo import optimizers

Note that when using a gradient-based technique, you must provide gradient evaluation options for all objective and constraint functions, by adding code to handle the optional ``der`` input.

def f(x, sx, der=0):
    # When using gradient-based solvers, define extra if-cases for
    # handling der=1 (calculate df/dx) and der=2 (caldculate df/dsx).

GPS Search Techniques (gradient-free)

.. automodule:: optimizers.gps_search

.. autoclass:: LocalGPS
   :member-order: bysource
   :members:

   .. automethod:: __init__

.. autoclass:: GlobalGPS
   :member-order: bysource
   :members:

   .. automethod:: __init__

Random Search Techniques (gradient-free)

.. automodule:: optimizers.random_search

.. autoclass:: RandomSearch
   :member-order: bysource
   :members:

   .. automethod:: __init__

L-BFGS-B Variations (gradient-based)

.. automodule:: optimizers.lbfgsb

.. autoclass:: LBFGSB
   :member-order: bysource
   :members:

   .. automethod:: __init__

.. autoclass:: TR_LBFGSB
   :member-order: bysource
   :members:

   .. automethod:: __init__