Optimization and root finding (scipy.optimize)

Optimization

Local Optimization

The minimize function supports the following methods:

• minimize(method=’Nelder-Mead’)
• minimize(method=’Powell’)
• minimize(method=’CG’)
• minimize(method=’BFGS’)
• minimize(method=’Newton-CG’)
• minimize(method=’L-BFGS-B’)
• minimize(method=’TNC’)
• minimize(method=’COBYLA’)
• minimize(method=’SLSQP’)
• minimize(method=’dogleg’)
• minimize(method=’trust-ncg’)

The minimize_scalar function supports the following methods:

• minimize_scalar(method=’brent’)
• minimize_scalar(method=’bounded’)
• minimize_scalar(method=’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:

Constrained multivariate methods:

Univariate (scalar) minimization methods:

Equation (Local) Minimizers

Global Optimization

Rosenbrock function

Fitting

Root finding

Scalar functions

Fixed point finding:

Multidimensional

General nonlinear solvers:

The root function supports the following methods:

• root(method=’hybr’)
• root(method=’lm’)
• root(method=’broyden1’)
• root(method=’broyden2’)
• root(method=’anderson’)
• root(method=’linearmixing’)
• root(method=’diagbroyden’)
• root(method=’excitingmixing’)
• root(method=’krylov’)
• root(method=’df-sane’)

Large-scale nonlinear solvers:

Simple iterations:

Additional information on the nonlinear solvers

Linear Programming

Simplex Algorithm:

The linprog function supports the following methods:

• linprog(method=’Simplex’)

Utilities