`); SciPySciPy Installing User Guide API reference Building from source Development Release notes GitHubGitHub TwitterTwitter Installing User Guide API reference Building from source Development Release notes GitHubGitHub TwitterTwitterSection Navigationscipyscipy.clusterscipy.constantsscipy.datasetsscipy.differentiatescipy.fftscipy.fftpackscipy.integratescipy.interpolatescipy.ioscipy.linalgscipy.ndimagescipy.odrscipy.optimizescipy.signalscipy.sparsescipy.spatialscipy.specialscipy.st..." />`); SciPySciPy Installing User Guide API reference Building from source Development Release notes GitHubGitHub TwitterTwitter Installing User Guide API reference Building from source Development Release notes GitHubGitHub TwitterTwitterSection Navigationscipyscipy.clusterscipy.constantsscipy.datasetsscipy.differentiatescipy.fftscipy.fftpackscipy.integratescipy.interpolatescipy.ioscipy.linalgscipy.ndimagescipy.odrscipy.optimizescipy.signalscipy.sparsescipy.spatialscipy.specialscipy.st..." /> minimize_scalar(method=’brent’) — SciPy v1.15.3 Manual

minimize_scalar(method=’brent’)#

scipy.optimize.minimize_scalar(fun, bracket=None, bounds=None, args=(), method=None, tol=None, options=None)

See also

For documentation for the rest of the parameters, see scipy.optimize.minimize_scalar

Options:
——-
maxiterint

Maximum number of iterations to perform.

xtolfloat

Relative error in solution xopt acceptable for convergence.

dispint, optional

If non-zero, print messages.

0 : no message printing.

1 : non-convergence notification messages only.

2 : print a message on convergence too.

3 : print iteration results.

Notes

Uses inverse parabolic interpolation when possible to speed up convergence of golden section method.