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java.lang.Objectorg.ejml.alg.dense.linsol.LinearSolverAbstract
org.ejml.alg.dense.linsol.qr.LinearSolverQrHouseTran
public class LinearSolverQrHouseTran
QR decomposition can be used to solve for systems. However, this is not as computationally efficient as LU decomposition and costs about 3n2 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
A column major decomposition is used in this solver.
| Field Summary | |
|---|---|
protected int |
maxCols
|
protected int |
maxRows
|
| Fields inherited from class org.ejml.alg.dense.linsol.LinearSolverAbstract |
|---|
A, numCols, numRows |
| Constructor Summary | |
|---|---|
LinearSolverQrHouseTran()
Creates a linear solver that uses QR decomposition. |
|
| Method Summary | |
|---|---|
boolean |
modifiesA()
Returns true if the passed in matrix to LinearSolver.setA(org.ejml.data.Matrix64F)
is modified. |
boolean |
modifiesB()
Returns true if the passed in 'B' matrix to LinearSolver.solve(org.ejml.data.Matrix64F, org.ejml.data.Matrix64F)
is modified. |
double |
quality()
Returns a very quick to compute measure of how singular the system is. |
boolean |
setA(DenseMatrix64F A)
Performs QR decomposition on A |
void |
setMaxSize(int maxRows,
int maxCols)
|
void |
solve(DenseMatrix64F B,
DenseMatrix64F X)
Solves for X using the QR decomposition. |
| Methods inherited from class org.ejml.alg.dense.linsol.LinearSolverAbstract |
|---|
_setA, getA, invert |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
protected int maxRows
protected int maxCols
| Constructor Detail |
|---|
public LinearSolverQrHouseTran()
| Method Detail |
|---|
public void setMaxSize(int maxRows,
int maxCols)
public boolean setA(DenseMatrix64F A)
A - not modified.
public double quality()
LinearSolverReturns 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.
public void solve(DenseMatrix64F B,
DenseMatrix64F X)
B - A matrix that is n by m. Not modified.X - An n by m matrix where the solution is written to. Modified.public boolean modifiesA()
LinearSolverLinearSolver.setA(org.ejml.data.Matrix64F)
is modified.
public boolean modifiesB()
LinearSolverLinearSolver.solve(org.ejml.data.Matrix64F, org.ejml.data.Matrix64F)
is modified.
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