public class BlockMatrix64HouseholderQR extends Object implements QRDecomposition<BlockMatrix64F>
QR decomposition for BlockMatrix64F using householder reflectors. The decomposition is
performed by computing a QR decomposition for each block column as is normally done, see QRDecompositionHouseholder.
The reflectors are then combined and applied to the remainder of the matrix. This process is repeated
until all the block columns have been processed
The input matrix is modified and used to store the decomposition. Reflectors are stored in the lower triangle columns. The first element of the reflector is implicitly assumed to be one.
Each iteration can be sketched as follows:
QR_Decomposition( A(:,i-r to i) ) W=computeW( A(:,i-r to i) ) A(:,i:n) = (I + W*YT)TA(:,i:n)Where r is the block size, i is the submatrix being considered, A is the input matrix, Y is a matrix containing the reflectors just computed, and W is computed using
BlockHouseHolder.computeW_Column(int, org.ejml.data.D1Submatrix64F, org.ejml.data.D1Submatrix64F, double[], double[], int).
Based upon "Block Householder QR Factorization" pg 255 in "Matrix Computations" 3rd Ed. 1996 by Gene H. Golub and Charles F. Van Loan.
| Constructor and Description |
|---|
BlockMatrix64HouseholderQR() |
| Modifier and Type | Method and Description |
|---|---|
void |
applyQ(BlockMatrix64F B)
Multiplies the provided matrix by Q using householder reflectors.
|
void |
applyQ(BlockMatrix64F B,
boolean isIdentity)
Specialized version of applyQ() that allows the zeros in an identity matrix
to be taken advantage of depending on if isIdentity is true or not.
|
void |
applyQTran(BlockMatrix64F B)
Multiplies the provided matrix by QT using householder reflectors.
|
boolean |
decompose(BlockMatrix64F orig)
Computes the decomposition of the input matrix.
|
BlockMatrix64F |
getQ(BlockMatrix64F Q,
boolean compact)
Returns the Q matrix from the decomposition.
|
BlockMatrix64F |
getQR()
This is the input matrix after it has been overwritten with the decomposition.
|
BlockMatrix64F |
getR(BlockMatrix64F R,
boolean compact)
Returns the R matrix from the decomposition.
|
static BlockMatrix64F |
initializeQ(BlockMatrix64F Q,
int numRows,
int numCols,
int blockLength,
boolean compact)
Sanity checks the input or declares a new matrix.
|
boolean |
inputModified()
The input matrix is always modified.
|
void |
setSaveW(boolean saveW)
Sets if it should internally save the W matrix before performing the decomposition.
|
protected void |
updateA(D1Submatrix64F A)
A = (I + W YT)TA
A = A + Y (WTA) where A is a submatrix of the input matrix. |
public BlockMatrix64F getQR()
public void setSaveW(boolean saveW)
Sets if it should internally save the W matrix before performing the decomposition. Must be set before decomposition the matrix.
Saving W can result in about a 5% savings when solving systems around a height of 5k. The price is that it needs to save a matrix the size of the input matrix.
saveW - If the W matrix should be saved or not.public BlockMatrix64F getQ(BlockMatrix64F Q, boolean compact)
QRDecomposition
Returns the Q matrix from the decomposition. Should only
be called after DecompositionInterface.decompose(org.ejml.data.Matrix64F) has
been called.
If parameter Q is not null, then that matrix is used to store the Q matrix. Otherwise a new matrix is created.
getQ in interface QRDecomposition<BlockMatrix64F>Q - If not null then the Q matrix is written to it. Modified.compact - If true an m by n matrix is created, otherwise n by n.public static BlockMatrix64F initializeQ(BlockMatrix64F Q, int numRows, int numCols, int blockLength, boolean compact)
public void applyQ(BlockMatrix64F B)
Multiplies the provided matrix by Q using householder reflectors. This is more efficient that computing Q then applying it to the matrix.
B = Q * B
B - Matrix which Q is applied to. Modified.public void applyQ(BlockMatrix64F B, boolean isIdentity)
B - isIdentity - If B is an identity matrix.public void applyQTran(BlockMatrix64F B)
Multiplies the provided matrix by QT using householder reflectors. This is more efficient that computing Q then applying it to the matrix.
Q = Q*(I - γ W*Y^T)
QR = A => R = Q^T*A = (Q3^T * (Q2^T * (Q1^t * A)))
B - Matrix which Q is applied to. Modified.public BlockMatrix64F getR(BlockMatrix64F R, boolean compact)
QRDecomposition
Returns the R matrix from the decomposition. Should only be
called after DecompositionInterface.decompose(org.ejml.data.Matrix64F) has been.
If setZeros is true then an n × m matrix is required and all the elements are set. If setZeros is false then the matrix must be at least m × m and only the upper triangular elements are set.
If parameter R is not null, then that matrix is used to store the R matrix. Otherwise a new matrix is created.
getR in interface QRDecomposition<BlockMatrix64F>R - If not null then the R matrix is written to it. Modified.compact - If true only the upper triangular elements are setpublic boolean decompose(BlockMatrix64F orig)
DecompositionInterfaceDecompositionInterface.inputModified() will return true and the matrix should not be
modified until the decomposition is no longer needed.decompose in interface DecompositionInterface<BlockMatrix64F>orig - The matrix which is being decomposed. Modification is implementation dependent.protected void updateA(D1Submatrix64F A)
A = (I + W YT)TA
A = A + Y (WTA)
where A is a submatrix of the input matrix.
public boolean inputModified()
inputModified in interface DecompositionInterface<BlockMatrix64F>Copyright © 2013. All Rights Reserved.