All Classes and Interfaces
Class
Description
A solver for QR decomposition that can efficiently modify the previous decomposition when
data is added or removed.
In many situations solutions to linear systems that share many of the same data points are needed.
Generic interface for wrapping a
FMatrixRBlock decomposition for
processing of FMatrixRMaj.
Base class for QR pivot based pseudo inverse classes.
Performs a
BidiagonalDecomposition_F32 using
householder reflectors.
Performs a
BidiagonalDecomposition_F32 using householder reflectors.BidiagonalDecomposition_F32 specifically designed for tall matrices.BidiagonalDecomposition_F32 specifically designed for tall matrices.
Contains various helper functions for performing a block matrix QR decomposition.
Contains various helper functions for performing a block matrix QR decomposition.
Wrapper around
CholeskyOuterForm_FDRB that allows
it to process FMatrixRMaj.This is an implementation of Cholesky that processes internal submatrices as blocks.
This is an abstract class for a Cholesky decomposition.
This implementation of a Cholesky decomposition using the inner-product form.
This variant on the Cholesky decomposition avoid the need to take the square root
by performing the following decomposition:
L*D*LT=A
where L is a lower triangular matrix with zeros on the diagonal.
L*D*LT=A
where L is a lower triangular matrix with zeros on the diagonal.
Block Cholesky using outer product form.
Block Cholesky using outer product form.
Linear solver that uses a block cholesky decomposition.
Linear solver that uses a block cholesky decomposition.
Common matrix operations for fixed sized matrices which are 2 x 2 or 2 element vectors.
Common matrix operations for fixed sized matrices which are 3 x 3 or 3 element vectors.
Common matrix operations for fixed sized matrices which are 4 x 4 or 4 element vectors.
Common matrix operations for fixed sized matrices which are 5 x 5 or 5 element vectors.
Common matrix operations for fixed sized matrices which are 6 x 6 or 6 element vectors.
Common matrix operations are contained here.
Functions from
CommonOps_FDRM with concurrent implementations.Contains operations specific to covariance matrices.
Generates random vectors based on a zero mean multivariate Gaussian distribution.
Contains operations related to creating and evaluating the quality of common matrix decompositions.
Contains concurrent implementations of different decompositions.
Computes the determinant of a matrix using Laplace expansion.
Additional functions related to eigenvalues and eigenvectors of a matrix.
The power method is an iterative method that can be used to find dominant eigen vector in
a matrix.
Renders a matrix as an image.
Functions for visualizing matrices in a GUI matrices.
Finds the decomposition of a matrix in the form of:
A = OHOT
where A is an m by m matrix, O is an orthogonal matrix, and H is an upper Hessenberg matrix.
A = OHOT
where A is an m by m matrix, O is an orthogonal matrix, and H is an upper Hessenberg matrix.
Concurrent implementation of
HessenbergSimilarDecomposition_FDRM.Implementations of common ops routines for
FMatrixRMaj.Implementations of common ops routines for
FMatrixRMaj.Performs a cholesky decomposition on an individual inner block.
Matrix multiplication for the inner row major blocks, typically inside of a
FMatrixRBlock.Performs rank-n update operations on the inner blocks of a
FMatrixRBlock
It is assumed and not checked that the submatrices are aligned along the matrix's blocks.Performs rank-n update operations on the inner blocks of a
FMatrixRBlock
It is assumed and not checked that the submatrices are aligned along the matrix's blocks.
Contains triangular solvers for inner blocks of a
FMatrixRBlock.A matrix can be easily inverted by solving a system with an identify matrix.
Wrapper that allows
FMatrixRBlock to implements LinearSolverDense.
An abstract class that provides some common functionality and a default implementation
of invert that uses the solve function of the child class.
A wrapper around
CholeskyDecomposition_F32(FMatrixRBlock) that allows
it to be easily used with FMatrixRMaj.A factory for generating solvers for systems of the form A*x=b, where A and B are known and x is unknown.
A factory for generating solvers for systems of the form A*x=b, where A and B are known and x is unknown.
For each column in the B matrix it makes a copy, which is then solved for and
writen into X.
To avoid cpu cache issues the order in which the arrays are traversed have been changed.
A solver for a generic QR decomposition algorithm.
Wrapper around
QrHouseHolderSolver_FDRB that allows it to process
FMatrixRMaj.
QR decomposition can be used to solve for systems.
QR decomposition can be used to solve for systems.
Concurrent extension of
LinearSolverQrHouseCol_FDRM.
QR decomposition can be used to solve for systems.
Performs a pseudo inverse solver using the
QRColPivDecompositionHouseholderColumn_FDRM decomposition
directly.Solver which uses an unrolled inverse to compute the inverse.
An LU decomposition algorithm that originally came from Jama.
Contains common data structures and operations for LU decomposition algorithms.
Matrix features for fixed sized matrices which are 2 x 2 or 2 element vectors.
Matrix features for fixed sized matrices which are 3 x 3 or 3 element vectors.
Matrix features for fixed sized matrices which are 4 x 4 or 4 element vectors.
Matrix features for fixed sized matrices which are 5 x 5 or 5 element vectors.
Matrix features for fixed sized matrices which are 6 x 6 or 6 element vectors.
Used to compute features that describe the structure of a matrix.
This class contains various types of matrix matrix multiplication operations for
FMatrix1Row.
This class contains various types of matrix matrix multiplication operations for
FMatrix1Row.
Matrix multiplication for
FMatrixRBlock.
Matrix multiplication for
FMatrixRBlock.
Specialized operations for performing inner and outer products for matrices.
Various operations on
FMatrixRBlock.Various operations on
FMatrixRBlock.
This class contains various types of matrix vector multiplcation operations for
FMatrixRMaj.Matrix norm related operations for fixed sized matrices of size 2.
Matrix norm related operations for fixed sized matrices of size 3.
Matrix norm related operations for fixed sized matrices of size 4.
Matrix norm related operations for fixed sized matrices of size 5.
Matrix norm related operations for fixed sized matrices of size 6.
Norms are a measure of the size of a vector or a matrix.
Performs QR decomposition with column pivoting.
Wrapper that allows
QRDecomposition(FMatrixRBlock) to be used
as a QRDecomposition(FMatrixRMaj).
QR decomposition for
FMatrixRBlock using householder reflectors.
This variation of QR decomposition uses reflections to compute the Q matrix.
QR decomposition for
FMatrixRBlock using householder reflectors.
Householder QR decomposition is rich in operations along the columns of the matrix.
Concurrent extension of
QRDecompositionHouseholderColumn_FDRM.
Householder QR decomposition is rich in operations along the columns of the matrix.
Contains different functions that are useful for computing the QR decomposition of a matrix.
Contains different functions that are useful for computing the QR decomposition of a matrix.
A solver for
QRDecompositionHouseholder_FDRB.
A solver for
QRDecompositionHouseholder_MT_FDRB.
The effects of adding and removing rows from the A matrix in a QR decomposition can
be computed much faster than simply recomputing the whole decomposition.
Contains a list of functions for creating random row real matrices and vectors with different structures.
Reduction to RREF using Gauss-Jordan elimination with row (partial) pivots.
Wraps around a
SingularValueDecomposition and ensures that the input is not modified.Operations related to singular value decomposition.
Uses QR decomposition to find the null-space for a matrix of any shape if the number of
singular values is known.
Uses QR decomposition to find the null-space for a matrix of any shape if the number of
singular values is known.=
A pseudo inverse solver for a generic QR column pivot decomposition algorithm.
The pseudo-inverse is typically used to solve over determined system for which there is no unique solution.
x=inv(ATA)ATb
where A ∈ ℜ m × n and m ≥ n.
x=inv(ATA)ATb
where A ∈ ℜ m × n and m ≥ n.
This contains less common or more specialized matrix operations.
Operations that are performed on a submatrix inside a larger matrix.
Computes the QR decomposition of a bidiagonal matrix.
Computes the Singular value decomposition of a matrix using the implicit QR algorithm
for singular value decomposition.
Concurrent version of
SvdImplicitQrDecompose_FDRMChecks to see what type of matrix is being decomposed and calls different eigenvalue decomposition
algorithms depending on the results.
Computes the eigenvalues and eigenvectors of a symmetric tridiagonal matrix using the symmetric QR algorithm.
Computes the eigenvalues and eigenvectors of a real symmetric matrix using the symmetric implicit QR algorithm.
A helper class for the symmetric matrix implicit QR algorithm for eigenvalue decomposition.
Low level transpose algorithms.
Low level transpose algorithms.
Contains triangular solvers for
FMatrixRBlock block aligned sub-matrices.
This contains algorithms for solving systems of equations where T is a
non-singular triangular matrix:
T*x = b
where x and b are vectors, and T is an n by n matrix.
T*x = b
where x and b are vectors, and T is an n by n matrix.
Contains triangular solvers for
FMatrixRBlock block aligned sub-matrices.Wrapper around a block implementation of TridiagonalSimilarDecomposition_F32
Tridiagonal similar decomposition for block matrices.
Performs a
similar tridiagonal decomposition on a square symmetric input matrix.
Tridiagonal similar decomposition for block matrices.
Concurrent implementation of
TridiagonalDecompositionHouseholder_FDRM
A straight forward implementation from "Fundamentals of Matrix Computations," Second Edition.
This is only saved to provide a point of reference in benchmarks.
This is only saved to provide a point of reference in benchmarks.
Performs an unrolled lower cholesky decomposition for small matrices.
This code was auto generated by GenerateDeterminantFromMinor and should not be modified
directly.
Unrolled inverse from minor for DDRM type matrices.
Helper functions for generic decompsotions.
Math operations for inner vectors (row and column) inside of block matrices:
scale: bi = α*ai
div: i = ai/α
add: ci = α*ai + βBi
dot: c = sum ai*bi
scale: bi = α*ai
div: i = ai/α
add: ci = α*ai + βBi
dot: c = sum ai*bi
Operations that involve multiplication of two vectors.
Finds the eigenvalue decomposition of an arbitrary square matrix using the implicit float-step QR algorithm.
The float step implicit Eigenvalue decomposition algorithm is fairly complicated and needs to be designed so that
it can handle several special cases.