December 2017
Intermediate to advanced
386 pages
10h 42m
English
Content preview from SciPy Recipes
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Getting ready
The following code is required to get ready before we proceed:
scipy.optimize.newton_krylov(F, xin, iter=None, rdiff=None, method='lgmres', inner_maxiter=20, inner_M=None, outer_k=10, verbose=False, maxiter=None, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None, tol_norm=None, line_search='armijo', callback=None, **kw)
In the following table can see the parameters and their description of the Anderson method
| Parameters |
F: function(x) -> f. Function whose root to find; should take and return an array-like object. xin: array_like. Initial guess for the solution. rdiff: float, optional. Relative step size to use in numerical differentiation. method: {'lgmres', 'gmres', 'bicgstab', 'cgs', 'minres'} or function. Krylov method ... |
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.