Class LinearSolverQrHouse_D64
- java.lang.Object
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- org.ejml.alg.dense.linsol.LinearSolverAbstract_D64
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- org.ejml.alg.dense.linsol.qr.LinearSolverQrHouse_D64
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- All Implemented Interfaces:
- LinearSolver<DenseMatrix64F>
public class LinearSolverQrHouse_D64 extends LinearSolverAbstract_D64
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
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Constructor Summary
Constructors Constructor and Description LinearSolverQrHouse_D64()Creates a linear solver that uses QR decomposition.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method and Description QRDecomposition<DenseMatrix64F>getDecomposition()If a decomposition class was used internally then this will return that class.booleanmodifiesA()Returns true if the passed in matrix toLinearSolver.setA(org.ejml.data.Matrix)is modified.booleanmodifiesB()Returns true if the passed in 'B' matrix toLinearSolver.solve(org.ejml.data.Matrix, org.ejml.data.Matrix)is modified.doublequality()Returns a very quick to compute measure of how singular the system is.booleansetA(DenseMatrix64F A)Performs QR decomposition on AvoidsetMaxSize(int maxRows)voidsolve(DenseMatrix64F B, DenseMatrix64F X)Solves for X using the QR decomposition.-
Methods inherited from class org.ejml.alg.dense.linsol.LinearSolverAbstract_D64
getA, invert
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Constructor Detail
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LinearSolverQrHouse_D64
public LinearSolverQrHouse_D64()
Creates a linear solver that uses QR decomposition.
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Method Detail
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setMaxSize
public void setMaxSize(int maxRows)
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setA
public boolean setA(DenseMatrix64F A)
Performs QR decomposition on A- Parameters:
A- not modified.- Returns:
- true if it can be processed.
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quality
public double quality()
Description copied from interface: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.
- Returns:
- The quality of the linear system.
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solve
public void solve(DenseMatrix64F B, DenseMatrix64F X)
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.
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modifiesA
public boolean modifiesA()
Description copied from interface:LinearSolverReturns true if the passed in matrix toLinearSolver.setA(org.ejml.data.Matrix)is modified.- Returns:
- true if A is modified in setA().
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modifiesB
public boolean modifiesB()
Description copied from interface:LinearSolverReturns true if the passed in 'B' matrix toLinearSolver.solve(org.ejml.data.Matrix, org.ejml.data.Matrix)is modified.- Returns:
- true if B is modified in solve(B,X).
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getDecomposition
public QRDecomposition<DenseMatrix64F> getDecomposition()
Description copied from interface:LinearSolverIf 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.
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