Class LinearSolverQrHouse_DDRM
 All Implemented Interfaces:
LinearSolver<DMatrixRMaj,
,DMatrixRMaj> LinearSolverDense<DMatrixRMaj>
QR decomposition can be used to solve for systems. However, this is not as computationally efficient as LU decomposition and costs about 3n^{2} flops.
It solve for x by first multiplying b by the transpose of Q then solving for the result.
QRx=b
Rx=Q^T b

Field Summary
Fields inherited from class org.ejml.dense.row.linsol.LinearSolverAbstract_DDRM
A, numCols, numRows

Constructor Summary
ConstructorDescriptionCreates a linear solver that uses QR decomposition. 
Method Summary
Modifier and TypeMethodDescriptionIf a decomposition class was used internally then this will return that class.boolean
Returns true if the passed in matrix toLinearSolver.setA(Matrix)
is modified.boolean
Returns true if the passed in 'B' matrix toLinearSolver.solve(Matrix, Matrix)
is modified.double
quality()
Returns a very quick to compute measure of how singular the system is.boolean
setA
(DMatrixRMaj A) Performs QR decomposition on Avoid
setMaxSize
(int maxRows) void
solve
(DMatrixRMaj B, DMatrixRMaj X) Solves for X using the QR decomposition.Methods inherited from class org.ejml.dense.row.linsol.LinearSolverAbstract_DDRM
_setA, getA, invert

Constructor Details

LinearSolverQrHouse_DDRM
public LinearSolverQrHouse_DDRM()Creates a linear solver that uses QR decomposition.


Method Details

setMaxSize
public void setMaxSize(int maxRows) 
setA
Performs QR decomposition on A Parameters:
A
 not modified. Returns:
 true if it can be processed.

quality
public double quality()Description copied from interface:LinearSolver
Returns a very quick to compute measure of how singular the system is. This measure will be invariant to the scale of the matrix and always be positive, with larger values indicating it is less singular. If not supported by the solver then the runtime exception IllegalArgumentException is thrown. This is NOT the matrix's condition.
How this function is implemented is not specified. One possible implementation is the following: In many decompositions a triangular matrix is extracted. The determinant of a triangular matrix is easily computed and once normalized to be scale invariant and its absolute value taken it will provide functionality described above.
 Returns:
 The quality of the linear system.

solve
Solves for X using the QR decomposition. Parameters:
B
 A matrix that is n by m. Not modified.X
 An n by m matrix where the solution is writen to. Modified.

modifiesA
public boolean modifiesA()Description copied from interface:LinearSolver
Returns true if the passed in matrix toLinearSolver.setA(Matrix)
is modified. Returns:
 true if A is modified in setA().

modifiesB
public boolean modifiesB()Description copied from interface:LinearSolver
Returns true if the passed in 'B' matrix toLinearSolver.solve(Matrix, Matrix)
is modified. Returns:
 true if B is modified in solve(B,X).

getDecomposition
Description copied from interface:LinearSolver
If a decomposition class was used internally then this will return that class. Most linear solvers decompose the input matrix into a more simplistic form. However some solutions do not require decomposition, e.g. inverse by minor. Returns:
 Internal decomposition class. If there is none then null.
