# scipy.linalg.polar¶

scipy.linalg.polar(a, side='right')[source]

Compute the polar decomposition.

Returns the factors of the polar decomposition  u and p such that a = up (if side is “right”) or a = pu (if side is “left”), where p is positive semidefinite. Depending on the shape of a, either the rows or columns of u are orthonormal. When a is a square array, u is a square unitary array. When a is not square, the “canonical polar decomposition”  is computed.

Parameters
a(m, n) array_like

The array to be factored.

side{‘left’, ‘right’}, optional

Determines whether a right or left polar decomposition is computed. If side is “right”, then a = up. If side is “left”, then a = pu. The default is “right”.

Returns
u(m, n) ndarray

If a is square, then u is unitary. If m > n, then the columns of a are orthonormal, and if m < n, then the rows of u are orthonormal.

pndarray

p is Hermitian positive semidefinite. If a is nonsingular, p is positive definite. The shape of p is (n, n) or (m, m), depending on whether side is “right” or “left”, respectively.

References

1(1,2)

R. A. Horn and C. R. Johnson, “Matrix Analysis”, Cambridge University Press, 1985.

2(1,2)

N. J. Higham, “Functions of Matrices: Theory and Computation”, SIAM, 2008.

Examples

>>> from scipy.linalg import polar
>>> a = np.array([[1, -1], [2, 4]])
>>> u, p = polar(a)
>>> u
array([[ 0.85749293, -0.51449576],
[ 0.51449576,  0.85749293]])
>>> p
array([[ 1.88648444,  1.2004901 ],
[ 1.2004901 ,  3.94446746]])


A non-square example, with m < n:

>>> b = np.array([[0.5, 1, 2], [1.5, 3, 4]])
>>> u, p = polar(b)
>>> u
array([[-0.21196618, -0.42393237,  0.88054056],
[ 0.39378971,  0.78757942,  0.4739708 ]])
>>> p
array([[ 0.48470147,  0.96940295,  1.15122648],
[ 0.96940295,  1.9388059 ,  2.30245295],
[ 1.15122648,  2.30245295,  3.65696431]])
>>> u.dot(p)   # Verify the decomposition.
array([[ 0.5,  1. ,  2. ],
[ 1.5,  3. ,  4. ]])
>>> u.dot(u.T)   # The rows of u are orthonormal.
array([[  1.00000000e+00,  -2.07353665e-17],
[ -2.07353665e-17,   1.00000000e+00]])


Another non-square example, with m > n:

>>> c = b.T
>>> u, p = polar(c)
>>> u
array([[-0.21196618,  0.39378971],
[-0.42393237,  0.78757942],
[ 0.88054056,  0.4739708 ]])
>>> p
array([[ 1.23116567,  1.93241587],
[ 1.93241587,  4.84930602]])
>>> u.dot(p)   # Verify the decomposition.
array([[ 0.5,  1.5],
[ 1. ,  3. ],
[ 2. ,  4. ]])
>>> u.T.dot(u)  # The columns of u are orthonormal.
array([[  1.00000000e+00,  -1.26363763e-16],
[ -1.26363763e-16,   1.00000000e+00]])


#### Previous topic

scipy.linalg.cho_solve_banded

scipy.linalg.qr