/** * 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; }