Use Factor.cholesky_AAt_inplace() (or similar) toĪll methods in this section accept both sparse and dense matrices (or All it cares about are (1) which entries are non-zero, and (2) This function ignores the actual contents of the \(AA'\), but does not factor it (i.e., it performs a “symbolicĬholesky decomposition”). analyze_AAt ( A, mode="auto", ordering_method="default", use_long=None ) ¶ Use Factor.cholesky_inplace() (or similar) to Operations on this object will fail, because it does not yet hold a fullĭecomposition. Use_long is None try to estimate if long type is needed.Ī Factor object representing the analysis. (32 bit) should be used for the indices of the sparse matrices. use_long – Specifies if the long type (64 bit) or the int type.See the CHOLMOD documentation for more details. “nesdis”, “colamd”, “default” and “best”. (eventually) order the matrix A – one of “natural”, “amd”, “metis”, ordering_method – Specifies which ordering algorithm should be used to.See the CHOLMOD documentation for details on how “auto” chooses mode – Specifies which algorithm should be used to (eventually)Ĭompute the Cholesky decomposition – one of “simplicial”, “supernodal”,.All it cares about are (1) which entries are non-zero, and (2) whether This function ignores the actual contents of the matrixĪ. analyze ( A, mode="auto", ordering_method="default", use_long=None ) ¶Ĭomputes the optimal fill-reducing permutation for the symmetric matrixĪ, but does not factor it (i.e., it performs a “symbolic Choleskyĭecomposition”). Of the analyze functions, which perform only fill-reduction: sksparse.cholmod. However, some users may want to break the fill-reduction analysis andĪctual decomposition into separate steps, and instead begin with one Returns:Ī Factor object represented the decomposition. Ordering_method is passed to analyze_AAt(). Therefore it will be somewhat more efficient to construct your matrix inĬSR format (so that its transpose will be in CSC format). Will need to transpose your matrix before calling this function, and Note that if you are solving a conventional least-squares problem, you Where A is a sparse matrix, preferably in CSC format, and beta isĪny real scalar (usually 0 or 1). Matrix that has been factored (though this is rarely useful). A convenience function for explicitly computing the inverse of the.Convenience functions for computing the (log) determinant of the.The last case is useful when theĬolumns of A become available incrementally (e.g., due to memoryĬonstraints), or when many matrices with similar but non-identical So, the result is the Cholesky decomposition of
In-place ‘update’ and ‘downdate’ operations, for computing theĬholesky decomposition of a rank-k update of \(A\) and of.Then re-use it to efficiently decompose many matrices with the same The ability to perform the costly fill-reduction analysis once, and.A convenient and efficient interface for using this decomposition to.Real and complex sparse matrices \(A\), in any format supportedīy scipy.sparse. Computation of the Cholesky decomposition \(LL' =Ī\) or \(LDL' = A\) (with fill-reducing permutation) for both.Specifically, it exposes most of the capabilities of the CHOLMOD package, (as, for instance, commonly arise in least squares problems). This module provides efficient implementations of all the basic linearĪlgebra operations for sparse, symmetric, positive-definite matrices