public class LinearSolverSafe<T extends ReshapeMatrix> extends java.lang.Object implements LinearSolverDense<T>
Constructor and Description 

LinearSolverSafe(LinearSolverDense<T> alg) 
Modifier and Type  Method and Description 

<D extends DecompositionInterface> 
getDecomposition()
If a decomposition class was used internally then this will return that class.

void 
invert(T A_inv)
Computes the inverse of of the 'A' matrix passed into
LinearSolver.setA(Matrix)
and writes the results to the provided matrix. 
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(T A)
Specifies the A matrix in the linear equation.

void 
solve(T B,
T X)
Solves for X in the linear system, A*X=B.

public LinearSolverSafe(LinearSolverDense<T> alg)
alg
 The solver it is wrapped around.public boolean setA(T 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.
setA
in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>
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.
quality
in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>
public void solve(T B, T X)
LinearSolver
Solves for X in the linear system, A*X=B.
In some implementations 'B' and 'X' can be the same instance of a variable. Call
LinearSolver.modifiesB()
to determine if 'B' is modified.
solve
in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>
B
 A matrix ℜ ^{m × p}. Might be modified.X
 A matrix ℜ ^{n × p}, where the solution is written to. Modified.public void invert(T A_inv)
LinearSolverDense
LinearSolver.setA(Matrix)
and writes the results to the provided matrix. If 'A_inv' needs to be different from 'A'
is implementation dependent.invert
in interface LinearSolverDense<T extends ReshapeMatrix>
A_inv
 Where the inverted matrix saved. Modified.public boolean modifiesA()
LinearSolver
LinearSolver.setA(Matrix)
is modified.modifiesA
in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>
public boolean modifiesB()
LinearSolver
LinearSolver.solve(Matrix, Matrix)
is modified.modifiesB
in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>
public <D extends DecompositionInterface> D getDecomposition()
LinearSolver
getDecomposition
in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>
D
 Decomposition type
Copyright © 20092018 Peter Abeles