Sparse linear algebra (scipy.sparse.linalg)#
Abstract linear operators#
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 | Common interface for performing matrix vector products | 
| Return A as a LinearOperator. | 
Matrix Operations#
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 | Compute the inverse of a sparse arrays | 
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 | Compute the matrix exponential using Pade approximation. | 
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 | Compute the action of the matrix exponential of A on B. | 
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 | Raise a square matrix to the integer power, power. | 
Matrix norms#
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 | Norm of a sparse matrix | 
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 | Compute a lower bound of the 1-norm of a sparse array. | 
Solving linear problems#
Direct methods for linear equation systems:
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 | Solve the sparse linear system Ax=b, where b may be a vector or a matrix. | 
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 | Solve the equation  | 
| Returns 2-tuple indicating lower/upper triangular structure for sparse  | |
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 | Return the lower and upper bandwidth of a 2D numeric array. | 
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 | Return a function for solving a sparse linear system, with A pre-factorized. | 
| Warning for exactly singular matrices. | |
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 | Select default sparse direct solver to be used. | 
Iterative methods for linear equation systems:
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 | Solve  | 
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 | Solve  | 
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 | Solve  | 
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 | Solve  | 
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 | Solve  | 
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 | Solve  | 
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 | Solve  | 
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 | Solve  | 
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 | Solve  | 
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 | Solve  | 
Iterative methods for least-squares problems:
Matrix factorizations#
Eigenvalue problems:
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 | Find k eigenvalues and eigenvectors of the square matrix A. | 
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 | Find k eigenvalues and eigenvectors of the real symmetric square matrix or complex Hermitian matrix A. | 
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 | Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG). | 
Singular values problems:
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 | Partial singular value decomposition of a sparse matrix. | 
The svds function supports the following solvers:
Complete or incomplete LU factorizations
Sparse arrays with structure#
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 | The grid Laplacian in  | 
Exceptions#
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 | ARPACK iteration did not converge | 
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 | ARPACK error |