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()
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.
quality in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>public void solve(T B, T X)
LinearSolverSolves 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)
LinearSolverDenseLinearSolver.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()
LinearSolverLinearSolver.setA(Matrix)
is modified.modifiesA in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>public boolean modifiesB()
LinearSolverLinearSolver.solve(Matrix, Matrix)
is modified.modifiesB in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>public <D extends DecompositionInterface> D getDecomposition()
LinearSolvergetDecomposition in interface LinearSolver<T extends ReshapeMatrix,T extends ReshapeMatrix>D - Decomposition type