/**
* Solves for "X" in the equation this * X = B, where X is a DenseMatrix, "this" and "B" will be
* converted to a DenseMatrix (if not already)
*
* @param B AbstractMTJMatrix, will be converted to a DenseMatrix
* @return DenseMatrix of "X" in this * X = B
*/
public final Matrix solve(AbstractMTJMatrix B) {
DenseMatrix X = new DenseMatrix(this.getNumColumns(), B.getNumColumns());
DenseMatrix Bdense;
if (B instanceof DenseMatrix) {
Bdense = (DenseMatrix) B;
} else {
Bdense = new DenseMatrix(B);
}
DenseMatrix Adense;
if (this instanceof DenseMatrix) {
Adense = (DenseMatrix) this;
} else {
Adense = new DenseMatrix(this);
}
boolean usePseudoInverse = false;
try {
Adense.solveInto(Bdense, X);
usePseudoInverse = false;
} catch (MatrixSingularException e) {
Logger.getLogger(AbstractMTJMatrix.class.getName())
.log(Level.WARNING, "AbstractMTJMatrix.solve(): Matrix is singular.");
usePseudoInverse = true;
}
// Sometimes LAPACK will return NaNs or infs as the solutions, but MTJ
// won't throw the exception, so we need to check for this.
// If we detect this, then we'll use a pseudoinverse
if (!usePseudoInverse) {
for (int i = 0; i < X.getNumRows(); i++) {
for (int j = 0; j < X.getNumColumns(); j++) {
double v = X.getElement(i, j);
if (Double.isNaN(v) || Double.isInfinite(v)) {
Logger.getLogger(AbstractMTJMatrix.class.getName())
.log(Level.WARNING, "AbstractMTJMatrix.solve(): Solver produced invalid results.");
usePseudoInverse = true;
break;
}
}
if (usePseudoInverse) {
break;
}
}
}
if (usePseudoInverse) {
// The original LU solver produced a sucky answer, so let's use
// the absurdly expensive SVD least-squares solution
return Adense.pseudoInverse().times(Bdense);
}
return X;
}
