<commit_before>## Automatically adapted for scipy Oct 07, 2005 by convertcode.py

from __future__ import nested_scopes
# ******NOTICE***************
# optimize.py module by <NAME>
#
# You may copy and use this module as you see fit with no
# guarantee implied provided you keep this notice in all copies.
# *****END NOTICE************

# A collection of optimization algorithms.  Version 0.5
# CHANGES
#  Added fminbound (July 2001)
#  Added brute (Aug. 2002)
#  Finished line search satisfying strong Wolfe conditions (Mar. 2004)
#  Updated strong Wolfe conditions line search to use cubic-interpolation (Mar. 2004)

# Minimization routines
"""optimize.py

A collection of general-purpose optimization routines using Numeric


N-D Algorithms
===============
fmin        ---      Nelder-Mead Simplex algorithm (uses only function calls).
fmin_powell ---      Powell (direction set) method (uses only function calls).
fmin_cg     ---      Non-linear (Polak-Rubiere) conjugate gradient algorithm
                       (can use function and gradient).
fmin_bfgs   ---      Quasi-Newton method (Broyden-Fletcher-Goldfarb-Shanno
                       (can use function and gradient).
fmin_ncg    ---      Line-search Newton Conjugate Gradient (can use function,
                       gradient and hessian).


brute       ---      Perform a brute force search for the minimum
                       with final optimization if desired.

1-D Algorithms
===============
brent       ---      Use Brent's method (does not need inital guess)
fminbound   ---      Bounded minimization for scalar functions on an
                     interval using Brent's parabolic/golden_mean method.
golden      --       Use Golden Section method (does not need initial guess)

bracket     ---      Find a bracket containing the minimum.

<commit_msg>Fixes to optimize.py docstring<commit_after>## Automatically adapted for scipy Oct 07, 2005 by convertcode.py

from __future__ import nested_scopes
# ******NOTICE***************
# optimize.py module by <NAME>
#
# You may copy and use this module as you see fit with no
# guarantee implied provided you keep this notice in all copies.
# *****END NOTICE************

# A collection of optimization algorithms.  Version 0.5
# CHANGES
#  Added fminbound (July 2001)
#  Added brute (Aug. 2002)
#  Finished line search satisfying strong Wolfe conditions (Mar. 2004)
#  Updated strong Wolfe conditions line search to use cubic-interpolation (Mar. 2004)

# Minimization routines
"""optimize.py

A collection of general-purpose optimization routines


N-D Algorithms
===============
fmin        ---      Nelder-Mead Simplex algorithm (uses only function calls).
fmin_powell ---      Powell (direction set) method (uses only function calls).
fmin_cg     ---      Non-linear (Polak-Ribiere) conjugate gradient algorithm
                       (can use function and gradient).
fmin_bfgs   ---      Quasi-Newton method (Broyden-Fletcher-Goldfarb-Shanno
                       (can use function and gradient).
fmin_ncg    ---      Line-search Newton Conjugate Gradient (can use function,
                       gradient and hessian).
brute       ---      Perform a brute force search for the minimum
                       with final optimization if desired.

1-D Algorithms
===============
brent       ---      Use Brent's method (does not need inital guess)
fminbound   ---      Bounded minimization for scalar functions on an
                       interval using Brent's parabolic/golden mean method.
golden      ---      Use Golden Section method (does not need initial guess)
bracket     ---      Find a bracket containing the minimum.


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