public class LinearSolverChol_DDRM extends LinearSolverAbstract_DDRM
A, numCols, numRows
Constructor and Description 

LinearSolverChol_DDRM(CholeskyDecompositionCommon_DDRM decomposer) 
Modifier and Type  Method and Description 

CholeskyDecomposition_F64<DMatrixRMaj> 
getDecomposition()
If a decomposition class was used internally then this will return that class.

void 
invert(DMatrixRMaj inv)
Sets the matrix 'inv' equal to the inverse of the matrix that was decomposed.

boolean 
modifiesA()
Returns true if the passed in matrix to
LinearSolver.setA(Matrix)
is modified. 
boolean 
modifiesB()
Returns true if the passed in 'B' matrix to
LinearSolver.solve(Matrix, Matrix)
is modified. 
double 
quality()
Returns a very quick to compute measure of how singular the system is.

boolean 
setA(DMatrixRMaj A)
Specifies the A matrix in the linear equation.

void 
setToInverseL(double[] a)
Sets the matrix to the inverse using a lower triangular matrix.

void 
solve(DMatrixRMaj B,
DMatrixRMaj X)
Using the decomposition, finds the value of 'X' in the linear equation below:
A*x = b where A has dimension of n by n, x and b are n by m dimension. 
static void 
solveLower(DMatrixRMaj L,
DMatrixRMaj B,
DMatrixRMaj X,
double[] vv) 
_setA, getA
public LinearSolverChol_DDRM(CholeskyDecompositionCommon_DDRM decomposer)
public boolean setA(DMatrixRMaj A)
LinearSolver
Specifies the A matrix in the linear equation. A reference might be saved
and it might also be modified depending on the implementation. If it is modified
then LinearSolver.modifiesA()
will return true.
If this value returns true that does not guarantee a valid solution was generated. This is because some decompositions don't detect singular matrices.
A
 The 'A' matrix in the linear equation. Might be modified or save the reference.public double quality()
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.
public void solve(DMatrixRMaj B, DMatrixRMaj X)
Using the decomposition, finds the value of 'X' in the linear equation below:
A*x = b
where A has dimension of n by n, x and b are n by m dimension.
*Note* that 'b' and 'x' can be the same matrix instance.
B
 A matrix that is n by m. Not modified.X
 An n by m matrix where the solution is writen to. Modified.public static void solveLower(DMatrixRMaj L, DMatrixRMaj B, DMatrixRMaj X, double[] vv)
public void invert(DMatrixRMaj inv)
invert
in interface LinearSolverDense<DMatrixRMaj>
invert
in class LinearSolverAbstract_DDRM
inv
 Where the value of the inverse will be stored. Modified.public void setToInverseL(double[] a)
public boolean modifiesA()
LinearSolver
LinearSolver.setA(Matrix)
is modified.public boolean modifiesB()
LinearSolver
LinearSolver.solve(Matrix, Matrix)
is modified.public CholeskyDecomposition_F64<DMatrixRMaj> getDecomposition()
LinearSolver
Copyright © 20092018 Peter Abeles