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from scipy import optimize
>>> info(optimize)
Optimization Tools
==================
A collection of general-purpose optimization routines.
fmin -- Nelder-Mead Simplex algorithm
(uses only function calls)
fmin_powell -- Powell's (modified) level 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 (Broydon-Fletcher-Goldfarb-Shanno);
(can use function and gradient)
fmin_ncg -- Line-search Newton Conjugate Gradient (can use
function, gradient and Hessian).
leastsq -- Minimize the sum of squares of M equations in
N unknowns given a starting estimate.
Constrained Optimizers (multivariate)
fmin_l_bfgs_b -- Zhu, Byrd, and Nocedal's L-BFGS-B constrained optimizer
(if you use this please quote their papers -- see help)
fmin_tnc -- Truncated Newton Code originally written by Stephen Nash and
adapted to C by Jean-Sebastien Roy.
fmin_cobyla -- Constrained Optimization BY Linear Approximation
Global Optimizers
basinhopping -- Basin-hopping stochastic optimizer
differential_evolution -- Stochastic optimizer
brute -- Brute force searching optimizer
Scalar function minimizers
fminbound -- Bounded minimization of a scalar function.
brent -- 1-D function minimization using Brent method.
golden -- 1-D function minimization using Golden Section method
bracket -- Bracket a minimum (given two starting points)
Also a collection of general-purpose root-finding routines.
fsolve -- Non-linear multi-variable equation solver.
Scalar function solvers
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 -- Single-variable fixed-point solver.
A collection of general-purpose nonlinear multidimensional solvers.
broyden1 -- Broyden's first method - is a quasi-Newton-Raphson
method for updating an approximate Jacobian and then
inverting it
broyden2 -- Broyden's second method - the same as broyden1, but
updates the inverse Jacobian directly
broyden3 -- Broyden's second method - the same as broyden2, but
instead of directly computing the inverse Jacobian,
it remembers how to construct it using vectors, and
when computing inv(J)*F, it uses those vectors to
compute this product, thus avoiding the expensive NxN
matrix multiplication.
broyden_generalized -- Generalized Broyden's method, the same as broyden2,
but instead of approximating the full NxN Jacobian,
it construct it at every iteration in a way that
avoids the NxN matrix multiplication. This is not
as precise as broyden3.
anderson -- extended Anderson method, the same as the
broyden_generalized, but added w_0^2*I to before
taking inversion to improve the stability
anderson2 -- the Anderson method, the same as anderson, but
formulated differently
Utility Functions
line_search -- Return a step that satisfies the strong Wolfe conditions.
check_grad -- Check the supplied derivative using finite difference
techniques.
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