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Core Linear Algebra Tools
=========================
=============== ==========================================================
Linear algebra basics
==========================================================================
norm Vector or matrix norm
inv Inverse of a square matrix
solve Solve a linear system of equations
det Determinant of a square matrix
lstsq Solve linear least-squares problem
pinv Pseudo-inverse (Moore-Penrose) calculated using a singular
value decomposition
matrix_power Integer power of a square matrix
=============== ==========================================================
=============== ==========================================================
Eigenvalues and decompositions
==========================================================================
eig Eigenvalues and vectors of a square matrix
eigh Eigenvalues and eigenvectors of a Hermitian matrix
eigvals Eigenvalues of a square matrix
eigvalsh Eigenvalues of a Hermitian matrix
qr QR decomposition of a matrix
svd Singular value decomposition of a matrix
cholesky Cholesky decomposition of a matrix
=============== ==========================================================
=============== ==========================================================
Tensor operations
==========================================================================
tensorsolve Solve a linear tensor equation
tensorinv Calculate an inverse of a tensor
=============== ==========================================================
=============== ==========================================================
Exceptions
==========================================================================
LinAlgError Indicates a failed linear algebra operation
=============== ==========================================================
i( t __doc__( t *( t TesterN( R t infot linalgt
numpy.testingR t testt bench( ( ( s; /usr/lib64/python2.6/site-packages/numpy/linalg/__init__.pyt + s