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Module « scipy.linalg »

Fonction fiedler - module scipy.linalg

Signature de la fonction fiedler

def fiedler(a) 

Description

help(scipy.linalg.fiedler)

Returns a symmetric Fiedler matrix

Given an sequence of numbers `a`, Fiedler matrices have the structure
``F[i, j] = np.abs(a[i] - a[j])``, and hence zero diagonals and nonnegative
entries. A Fiedler matrix has a dominant positive eigenvalue and other
eigenvalues are negative. Although not valid generally, for certain inputs,
the inverse and the determinant can be derived explicitly as given in [1]_.

Parameters
----------
a : (..., n,) array_like
    Coefficient array. N-dimensional arrays are treated as a batch:
    each slice along the last axis is a 1-D coefficient array.

Returns
-------
F : (..., n, n) ndarray
    Fiedler matrix. For batch input, each slice of shape ``(n, n)``
    along the last two dimensions of the output corresponds with a
    slice of shape ``(n,)`` along the last dimension of the input.

See Also
--------
circulant, toeplitz

Notes
-----

.. versionadded:: 1.3.0

References
----------
.. [1] J. Todd, "Basic Numerical Mathematics: Vol.2 : Numerical Algebra",
    1977, Birkhauser, :doi:`10.1007/978-3-0348-7286-7`

Examples
--------
>>> import numpy as np
>>> from scipy.linalg import det, inv, fiedler
>>> a = [1, 4, 12, 45, 77]
>>> n = len(a)
>>> A = fiedler(a)
>>> A
array([[ 0,  3, 11, 44, 76],
       [ 3,  0,  8, 41, 73],
       [11,  8,  0, 33, 65],
       [44, 41, 33,  0, 32],
       [76, 73, 65, 32,  0]])

The explicit formulas for determinant and inverse seem to hold only for
monotonically increasing/decreasing arrays. Note the tridiagonal structure
and the corners.

>>> Ai = inv(A)
>>> Ai[np.abs(Ai) < 1e-12] = 0.  # cleanup the numerical noise for display
>>> Ai
array([[-0.16008772,  0.16666667,  0.        ,  0.        ,  0.00657895],
       [ 0.16666667, -0.22916667,  0.0625    ,  0.        ,  0.        ],
       [ 0.        ,  0.0625    , -0.07765152,  0.01515152,  0.        ],
       [ 0.        ,  0.        ,  0.01515152, -0.03077652,  0.015625  ],
       [ 0.00657895,  0.        ,  0.        ,  0.015625  , -0.00904605]])
>>> det(A)
15409151.999999998
>>> (-1)**(n-1) * 2**(n-2) * np.diff(a).prod() * (a[-1] - a[0])
15409152

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