Eigen  5.0.1-dev
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AccelerateSupport module
Sparse linear algebra » Reference » Support modules

Detailed Description

This module provides an interface to the Apple Accelerate library. It provides the seven following main factorization classes:

  • class AccelerateLLT: a Cholesky (LL^T) factorization.
  • class AccelerateLDLT: the default LDL^T factorization.
  • class AccelerateLDLTUnpivoted: a Cholesky-like LDL^T factorization with only 1x1 pivots and no pivoting
  • class AccelerateLDLTSBK: an LDL^T factorization with Supernode Bunch-Kaufman and static pivoting
  • class AccelerateLDLTTPP: an LDL^T factorization with full threshold partial pivoting
  • class AccelerateQR: a QR factorization
  • class AccelerateCholeskyAtA: a QR factorization without storing Q (equivalent to A^TA = R^T R)
#include <Eigen/AccelerateSupport>

In order to use this module, the Accelerate headers must be accessible from the include paths, and your binary must be linked to the Accelerate framework. The Accelerate library is only available on Apple hardware.

Note that many of the algorithms can be influenced by the UpLo template argument. All matrices are assumed to be symmetric. For example, the following creates an LDLT factorization where your matrix is symmetric (implicit) and uses the lower triangle:

AccelerateLDLT<SparseMatrix<float>, Lower> ldlt;

Typedefs

template<typename MatrixType>
using Eigen::AccelerateCholeskyAtA
 A QR factorization and solver based on Accelerate without storing Q (equivalent to A^TA = R^T R).
template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLDLT
 The default Cholesky (LDLT) factorization and solver based on Accelerate.
template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLDLTSBK
 A direct Cholesky (LDLT) factorization and solver based on Accelerate with Supernode Bunch-Kaufman and static pivoting.
template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLDLTTPP
 A direct Cholesky (LDLT) factorization and solver based on Accelerate with full threshold partial pivoting.
template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLDLTUnpivoted
 A direct Cholesky-like LDL^T factorization and solver based on Accelerate with only 1x1 pivots and no pivoting.
template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLLT
 A direct Cholesky (LLT) factorization and solver based on Accelerate.
template<typename MatrixType>
using Eigen::AccelerateQR
 A QR factorization and solver based on Accelerate.

Typedef Documentation

◆ AccelerateCholeskyAtA

template<typename MatrixType>
using Eigen::AccelerateCholeskyAtA

A QR factorization and solver based on Accelerate without storing Q (equivalent to A^TA = R^T R).

Warning
Only single and double precision real scalar types are supported by Accelerate
Template Parameters
MatrixType_the type of the sparse matrix A, it must be a SparseMatrix<>
See also
Sparse solver concept, class AccelerateCholeskyAtA

◆ AccelerateLDLT

template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLDLT

The default Cholesky (LDLT) factorization and solver based on Accelerate.

Warning
Only single and double precision real scalar types are supported by Accelerate
Template Parameters
MatrixType_the type of the sparse matrix A, it must be a SparseMatrix<>
UpLo_additional information about the matrix structure. Default is Lower.
See also
Sparse solver concept, class AccelerateLDLT

◆ AccelerateLDLTSBK

template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLDLTSBK

A direct Cholesky (LDLT) factorization and solver based on Accelerate with Supernode Bunch-Kaufman and static pivoting.

Warning
Only single and double precision real scalar types are supported by Accelerate
Template Parameters
MatrixType_the type of the sparse matrix A, it must be a SparseMatrix<>
UpLo_additional information about the matrix structure. Default is Lower.
See also
Sparse solver concept, class AccelerateLDLTSBK

◆ AccelerateLDLTTPP

template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLDLTTPP

A direct Cholesky (LDLT) factorization and solver based on Accelerate with full threshold partial pivoting.

Warning
Only single and double precision real scalar types are supported by Accelerate
Template Parameters
MatrixType_the type of the sparse matrix A, it must be a SparseMatrix<>
UpLo_additional information about the matrix structure. Default is Lower.
See also
Sparse solver concept, class AccelerateLDLTTPP

◆ AccelerateLDLTUnpivoted

template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLDLTUnpivoted

A direct Cholesky-like LDL^T factorization and solver based on Accelerate with only 1x1 pivots and no pivoting.

Warning
Only single and double precision real scalar types are supported by Accelerate
Template Parameters
MatrixType_the type of the sparse matrix A, it must be a SparseMatrix<>
UpLo_additional information about the matrix structure. Default is Lower.
See also
Sparse solver concept, class AccelerateLDLTUnpivoted

◆ AccelerateLLT

template<typename MatrixType, int UpLo = Lower>
using Eigen::AccelerateLLT

A direct Cholesky (LLT) factorization and solver based on Accelerate.

Warning
Only single and double precision real scalar types are supported by Accelerate
Template Parameters
MatrixType_the type of the sparse matrix A, it must be a SparseMatrix<>
UpLo_additional information about the matrix structure. Default is Lower.
See also
Sparse solver concept, class AccelerateLLT

◆ AccelerateQR

template<typename MatrixType>
using Eigen::AccelerateQR

A QR factorization and solver based on Accelerate.

Warning
Only single and double precision real scalar types are supported by Accelerate
Template Parameters
MatrixType_the type of the sparse matrix A, it must be a SparseMatrix<>
See also
Sparse solver concept, class AccelerateQR