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File Content: `init.py`

"""
=====================================================
Optimization and root finding (:mod:`scipy.optimize`)
=====================================================

.. currentmodule:: scipy.optimize

Optimization
============

Local Optimization
------------------

.. autosummary::
   :toctree: generated/

   minimize - Unified interface for minimizers of multivariate functions
   minimize_scalar - Unified interface for minimizers of univariate functions
   OptimizeResult - The optimization result returned by some optimizers
   OptimizeWarning - The optimization encountered problems

The `minimize` function supports the following methods:

.. toctree::

   optimize.minimize-neldermead
   optimize.minimize-powell
   optimize.minimize-cg
   optimize.minimize-bfgs
   optimize.minimize-newtoncg
   optimize.minimize-lbfgsb
   optimize.minimize-tnc
   optimize.minimize-cobyla
   optimize.minimize-slsqp
   optimize.minimize-dogleg
   optimize.minimize-trustncg

The `minimize_scalar` function supports the following methods:

.. toctree::

   optimize.minimize_scalar-brent
   optimize.minimize_scalar-bounded
   optimize.minimize_scalar-golden

The specific optimization method interfaces below in this subsection are
not recommended for use in new scripts; all of these methods are accessible
via a newer, more consistent interface provided by the functions above.

General-purpose multivariate methods:

.. autosummary::
   :toctree: generated/

   fmin - Nelder-Mead Simplex algorithm
   fmin_powell - Powell's (modified) level set method
   fmin_cg - Non-linear (Polak-Ribiere) conjugate gradient algorithm
   fmin_bfgs - Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno)
   fmin_ncg - Line-search Newton Conjugate Gradient

Constrained multivariate methods:

.. autosummary::
   :toctree: generated/

   fmin_l_bfgs_b - Zhu, Byrd, and Nocedal's constrained optimizer
   fmin_tnc - Truncated Newton code
   fmin_cobyla - Constrained optimization by linear approximation
   fmin_slsqp - Minimization using sequential least-squares programming
   differential_evolution - stochastic minimization using differential evolution

Univariate (scalar) minimization methods:

.. autosummary::
   :toctree: generated/

   fminbound - Bounded minimization of a scalar function
   brent - 1-D function minimization using Brent method
   golden - 1-D function minimization using Golden Section method

Equation (Local) Minimizers
---------------------------

.. autosummary::
   :toctree: generated/
   
   leastsq - Minimize the sum of squares of M equations in N unknowns
   least_squares - Feature-rich least-squares minimization.
   nnls - Linear least-squares problem with non-negativity constraint
   lsq_linear - Linear least-squares problem with bound constraints

Global Optimization
-------------------

.. autosummary::
   :toctree: generated/

   basinhopping - Basinhopping stochastic optimizer
   brute - Brute force searching optimizer
   differential_evolution - stochastic minimization using differential evolution

Rosenbrock function
-------------------

.. autosummary::
   :toctree: generated/

   rosen - The Rosenbrock function.
   rosen_der - The derivative of the Rosenbrock function.
   rosen_hess - The Hessian matrix of the Rosenbrock function.
   rosen_hess_prod - Product of the Rosenbrock Hessian with a vector.

Fitting
=======

.. autosummary::
   :toctree: generated/

   curve_fit -- Fit curve to a set of points

Root finding
============

Scalar functions
----------------
.. autosummary::
   :toctree: generated/

   brentq - quadratic interpolation Brent method
   brenth - Brent method, modified by Harris with hyperbolic extrapolation
   ridder - Ridder's method
   bisect - Bisection method
   newton - Secant method or Newton's method

Fixed point finding:

.. autosummary::
   :toctree: generated/

   fixed_point - Single-variable fixed-point solver

Multidimensional
----------------

General nonlinear solvers:

.. autosummary::
   :toctree: generated/

   root - Unified interface for nonlinear solvers of multivariate functions
   fsolve - Non-linear multi-variable equation solver
   broyden1 - Broyden's first method
   broyden2 - Broyden's second method

The `root` function supports the following methods:

.. toctree::

   optimize.root-hybr
   optimize.root-lm
   optimize.root-broyden1
   optimize.root-broyden2
   optimize.root-anderson
   optimize.root-linearmixing
   optimize.root-diagbroyden
   optimize.root-excitingmixing
   optimize.root-krylov
   optimize.root-dfsane

Large-scale nonlinear solvers:

.. autosummary::
   :toctree: generated/

   newton_krylov
   anderson

Simple iterations:

.. autosummary::
   :toctree: generated/

   excitingmixing
   linearmixing
   diagbroyden

:mod:`Additional information on the nonlinear solvers <scipy.optimize.nonlin>`

Linear Programming
==================

Simplex Algorithm:

.. autosummary::
   :toctree: generated/

   linprog -- Linear programming using the simplex algorithm
   linprog_verbose_callback -- Sample callback function for linprog

The `linprog` function supports the following methods:

.. toctree::

   optimize.linprog-simplex

Assignment problems:

.. autosummary::
   :toctree: generated/

   linear_sum_assignment -- Solves the linear-sum assignment problem


Utilities
=========

.. autosummary::
   :toctree: generated/

   approx_fprime - Approximate the gradient of a scalar function
   bracket - Bracket a minimum, given two starting points
   check_grad - Check the supplied derivative using finite differences
   line_search - Return a step that satisfies the strong Wolfe conditions

   show_options - Show specific options optimization solvers
   LbfgsInvHessProduct - Linear operator for L-BFGS approximate inverse Hessian

"""

from __future__ import division, print_function, absolute_import

from .optimize import *
from ._minimize import *
from ._root import *
from .minpack import *
from .zeros import *
from .lbfgsb import fmin_l_bfgs_b, LbfgsInvHessProduct
from .tnc import fmin_tnc
from .cobyla import fmin_cobyla
from .nonlin import *
from .slsqp import fmin_slsqp
from .nnls import nnls
from ._basinhopping import basinhopping
from ._linprog import linprog, linprog_verbose_callback
from ._hungarian import linear_sum_assignment
from ._differentialevolution import differential_evolution
from ._lsq import least_squares, lsq_linear


__all__ = [s for s in dir() if not s.startswith('_')]
from numpy.testing import Tester
test = Tester().test
bench = Tester().bench

Name	Size	Permission
__pycache__	---	0755
_lsq	---	0755
tests	---	0755
__init__.py	6526 bytes	0644
_basinhopping.py	26053 bytes	0644
_cobyla.cpython-35m-x86_64-linux-gnu.so	128896 bytes	0755
_differentialevolution.py	32558 bytes	0644
_group_columns.cpython-35m-x86_64-linux-gnu.so	141704 bytes	0755
_hungarian.py	9379 bytes	0644
_lbfgsb.cpython-35m-x86_64-linux-gnu.so	133600 bytes	0755
_linprog.py	37305 bytes	0644
_minimize.py	26435 bytes	0644
_minpack.cpython-35m-x86_64-linux-gnu.so	131488 bytes	0755
_nnls.cpython-35m-x86_64-linux-gnu.so	46656 bytes	0755
_numdiff.py	21209 bytes	0644
_root.py	26007 bytes	0644
_slsqp.cpython-35m-x86_64-linux-gnu.so	100568 bytes	0755
_spectral.py	7986 bytes	0644
_trustregion.py	8598 bytes	0644
_trustregion_dogleg.py	4449 bytes	0644
_trustregion_ncg.py	4646 bytes	0644
_tstutils.py	1322 bytes	0644
_zeros.cpython-35m-x86_64-linux-gnu.so	15760 bytes	0755
cobyla.py	9915 bytes	0644
lbfgsb.py	18069 bytes	0644
linesearch.py	24201 bytes	0644
minpack.py	30534 bytes	0644
minpack2.cpython-35m-x86_64-linux-gnu.so	47448 bytes	0755
moduleTNC.cpython-35m-x86_64-linux-gnu.so	40336 bytes	0755
nnls.py	1423 bytes	0644
nonlin.py	46681 bytes	0644
optimize.py	101006 bytes	0644
setup.py	3267 bytes	0644
slsqp.py	17983 bytes	0644
tnc.py	16533 bytes	0644
zeros.py	19912 bytes	0644

File Content: __init__.py

File Content: `init.py`