Eigen  3.3.90 (mercurial changeset 493691b29be1)
Eigen::LLT< _MatrixType, _UpLo > Class Template Reference

Detailed Description

template<typename _MatrixType, int _UpLo>
class Eigen::LLT< _MatrixType, _UpLo >

Standard Cholesky decomposition (LL^T) of a matrix and associated features.

Template Parameters
_MatrixTypethe type of the matrix of which we are computing the LL^T Cholesky decomposition
_UpLothe triangular part that will be used for the decompositon: Lower (default) or Upper. The other triangular part won't be read.

This class performs a LL^T Cholesky decomposition of a symmetric, positive definite matrix A such that A = LL^* = U^*U, where L is lower triangular.

While the Cholesky decomposition is particularly useful to solve selfadjoint problems like D^*D x = b, for that purpose, we recommend the Cholesky decomposition without square root which is more stable and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other situations like generalised eigen problems with hermitian matrices.

Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices, use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations has a solution.


MatrixXd A(3,3);
A << 4,-1,2, -1,6,0, 2,0,5;
cout << "The matrix A is" << endl << A << endl;
LLT<MatrixXd> lltOfA(A); // compute the Cholesky decomposition of A
MatrixXd L = lltOfA.matrixL(); // retrieve factor L in the decomposition
// The previous two lines can also be written as "L = A.llt().matrixL()"
cout << "The Cholesky factor L is" << endl << L << endl;
cout << "To check this, let us compute L * L.transpose()" << endl;
cout << L * L.transpose() << endl;
cout << "This should equal the matrix A" << endl;


The matrix A is
 4 -1  2
-1  6  0
 2  0  5
The Cholesky factor L is
    2     0     0
 -0.5   2.4     0
    1 0.209  1.99
To check this, let us compute L * L.transpose()
 4 -1  2
-1  6  0
 2  0  5
This should equal the matrix A

Performance: for best performance, it is recommended to use a column-major storage format with the Lower triangular part (the default), or, equivalently, a row-major storage format with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization step, and rank-updates can be up to 3 times slower.

This class supports the inplace decomposition mechanism.

Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered. Therefore, the strict lower part does not have to store correct values.

See also
MatrixBase::llt(), SelfAdjointView::llt(), class LDLT

Public Types

typedef Eigen::Index Index

Public Member Functions

const LLTadjoint () const
template<typename InputType >
LLT< MatrixType, _UpLo > & compute (const EigenBase< InputType > &a)
ComputationInfo info () const
 Reports whether previous computation was successful. More...
 LLT ()
 Default Constructor. More...
 LLT (Index size)
 Default Constructor with memory preallocation. More...
template<typename InputType >
 LLT (EigenBase< InputType > &matrix)
 Constructs a LDLT factorization from a given matrix. More...
Traits::MatrixL matrixL () const
const MatrixType & matrixLLT () const
Traits::MatrixU matrixU () const
template<typename VectorType >
LLT< _MatrixType, _UpLo > rankUpdate (const VectorType &v, const RealScalar &sigma)
RealScalar rcond () const
MatrixType reconstructedMatrix () const
template<typename Rhs >
const Solve< LLT, Rhs > solve (const MatrixBase< Rhs > &b) const

Member Typedef Documentation

◆ Index

template<typename _MatrixType, int _UpLo>
typedef Eigen::Index Eigen::LLT< _MatrixType, _UpLo >::Index
since Eigen 3.3

Constructor & Destructor Documentation

◆ LLT() [1/3]

template<typename _MatrixType, int _UpLo>
Eigen::LLT< _MatrixType, _UpLo >::LLT ( )

Default Constructor.

The default constructor is useful in cases in which the user intends to perform decompositions via LLT::compute(const MatrixType&).

◆ LLT() [2/3]

template<typename _MatrixType, int _UpLo>
Eigen::LLT< _MatrixType, _UpLo >::LLT ( Index  size)

Default Constructor with memory preallocation.

Like the default constructor but with preallocation of the internal data according to the specified problem size.

See also

◆ LLT() [3/3]

template<typename _MatrixType, int _UpLo>
template<typename InputType >
Eigen::LLT< _MatrixType, _UpLo >::LLT ( EigenBase< InputType > &  matrix)

Constructs a LDLT factorization from a given matrix.

This overloaded constructor is provided for inplace decomposition when MatrixType is a Eigen::Ref.

See also
LLT(const EigenBase&)

Member Function Documentation

◆ adjoint()

template<typename _MatrixType, int _UpLo>
const LLT& Eigen::LLT< _MatrixType, _UpLo >::adjoint ( ) const
the adjoint of *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.

This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:

x = decomposition.adjoint().solve(b)

◆ compute()

template<typename _MatrixType, int _UpLo>
template<typename InputType >
LLT<MatrixType,_UpLo>& Eigen::LLT< _MatrixType, _UpLo >::compute ( const EigenBase< InputType > &  a)

Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of matrix

a reference to *this


#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
Matrix2f A, b;
A << 2, -1, -1, 3;
b << 1, 2, 3, 1;
cout << "Here is the matrix A:\n" << A << endl;
cout << "Here is the right hand side b:\n" << b << endl;
cout << "Computing LLT decomposition..." << endl;
cout << "The solution is:\n" << llt.solve(b) << endl;
cout << "The matrix A is now:\n" << A << endl;
cout << "Computing LLT decomposition..." << endl;
cout << "The solution is now:\n" << llt.solve(b) << endl;


Here is the matrix A:
 2 -1
-1  3
Here is the right hand side b:
1 2
3 1
Computing LLT decomposition...
The solution is:
1.2 1.4
1.4 0.8
The matrix A is now:
 2 -1
-1  4
Computing LLT decomposition...
The solution is now:
    1  1.29
    1 0.571

◆ info()

template<typename _MatrixType, int _UpLo>
ComputationInfo Eigen::LLT< _MatrixType, _UpLo >::info ( ) const

Reports whether previous computation was successful.

Success if computation was succesful, NumericalIssue if the matrix.appears not to be positive definite.

◆ matrixL()

template<typename _MatrixType, int _UpLo>
Traits::MatrixL Eigen::LLT< _MatrixType, _UpLo >::matrixL ( ) const
a view of the lower triangular matrix L

◆ matrixLLT()

template<typename _MatrixType, int _UpLo>
const MatrixType& Eigen::LLT< _MatrixType, _UpLo >::matrixLLT ( ) const
the LLT decomposition matrix

TODO: document the storage layout

◆ matrixU()

template<typename _MatrixType, int _UpLo>
Traits::MatrixU Eigen::LLT< _MatrixType, _UpLo >::matrixU ( ) const
a view of the upper triangular matrix U

◆ rankUpdate()

template<typename _MatrixType, int _UpLo>
template<typename VectorType >
LLT<_MatrixType,_UpLo> Eigen::LLT< _MatrixType, _UpLo >::rankUpdate ( const VectorType &  v,
const RealScalar &  sigma 

Performs a rank one update (or dowdate) of the current decomposition. If A = LL^* before the rank one update, then after it we have LL^* = A + sigma * v v^* where v must be a vector of same dimension.

◆ rcond()

template<typename _MatrixType, int _UpLo>
RealScalar Eigen::LLT< _MatrixType, _UpLo >::rcond ( ) const
an estimate of the reciprocal condition number of the matrix of which *this is the Cholesky decomposition.

◆ reconstructedMatrix()

template<typename MatrixType , int _UpLo>
MatrixType Eigen::LLT< MatrixType, _UpLo >::reconstructedMatrix ( ) const
the matrix represented by the decomposition, i.e., it returns the product: L L^*. This function is provided for debug purpose.

◆ solve()

template<typename _MatrixType, int _UpLo>
template<typename Rhs >
const Solve<LLT, Rhs> Eigen::LLT< _MatrixType, _UpLo >::solve ( const MatrixBase< Rhs > &  b) const
the solution x of $ A x = b $ using the current decomposition of A.

Since this LLT class assumes anyway that the matrix A is invertible, the solution theoretically exists and is unique regardless of b.


typedef Matrix<float,Dynamic,2> DataMatrix;
// let's generate some samples on the 3D plane of equation z = 2x+3y (with some noise)
DataMatrix samples = DataMatrix::Random(12,2);
VectorXf elevations = 2*samples.col(0) + 3*samples.col(1) + VectorXf::Random(12)*0.1;
// and let's solve samples * [x y]^T = elevations in least square sense:
Matrix<float,2,1> xy
= (samples.adjoint() * samples).llt().solve((samples.adjoint()*elevations));
cout << xy << endl;


See also
solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()

The documentation for this class was generated from the following file: