public Matrix[] lu() { LUDecomposition lu = new LUDecomposition(matrix); Matrix l = new JamaDenseDoubleMatrix2D(lu.getL()); Matrix u = new JamaDenseDoubleMatrix2D(lu.getU()); int m = (int) getRowCount(); int[] piv = lu.getPivot(); Matrix p = new JamaDenseDoubleMatrix2D(m, m); for (int i = 0; i < m; i++) { p.setAsDouble(1, i, piv[i]); } return new Matrix[] { l, u, p }; }
/** Solve A*X = B @param B right hand side @return solution if A is square, least squares solution otherwise */ public Matrix solve (Matrix B) { return (m == n ? (new LUDecomposition(this)).solve(B) : (new QRDecomposition(this)).solve(B)); }
/** Matrix determinant @return determinant */ public double det () { return new LUDecomposition(this).det(); }
@Override public long process(BenchmarkMatrix[] inputs, BenchmarkMatrix[] outputs, long numTrials) { Matrix matA = inputs[0].getOriginal(); Matrix L = null; Matrix U = null; int pivot[] = null; long prev = System.nanoTime(); for( long i = 0; i < numTrials; i++ ) { LUDecomposition lu = matA.lu(); L = lu.getL(); U = lu.getU(); pivot = lu.getPivot(); } long elapsed = System.nanoTime()-prev; if( outputs != null ) { outputs[0] = new JamaBenchmarkMatrix(L); outputs[1] = new JamaBenchmarkMatrix(U); outputs[2] = new RowMajorBenchmarkMatrix(RowMajorOps.pivotMatrix(null, pivot, pivot.length, false)); } return elapsed; } }
final LUDecomposition decomp = new LUDecomposition( H ); if ( decomp.isNonsingular() ) final Matrix minusOffset = decomp.solve( g ); for ( int d = 0; d < n; ++d )
public static double[][] divideLU(double[][] v1, double[]... v2) { return LU(v2).solve(Matrix.constructWithCopy(v1)).getArray(); }
/** LU Decomposition @return LUDecomposition @see LUDecomposition */ public LUDecomposition lu () { return new LUDecomposition(this); }
throw new IllegalArgumentException("Matrix row dimensions must agree."); if (!this.isNonsingular()) { throw new RuntimeException("Matrix is singular.");
public static double[][] inverseLU(double[][] v1) { checkColumnDimension(v1, v1.length); return LU(v1).solve(Matrix.identity(v1.length, v1.length)).getArray(); }
public static LUDecomposition LU(double[][] v) { return new LUDecomposition(Matrix.constructWithCopy(v)); }
public Matrix[] lu() { LUDecomposition lu = new LUDecomposition(matrix); Matrix l = new JamaDenseDoubleMatrix2D(lu.getL()); Matrix u = new JamaDenseDoubleMatrix2D(lu.getU()); int m = (int) getRowCount(); int[] piv = lu.getPivot(); Matrix p = new JamaDenseDoubleMatrix2D(m, m); for (int i = 0; i < m; i++) { p.setAsDouble(1, i, piv[i]); } return new Matrix[] { l, u, p }; }
mA.set(1, 2, lona[1]); mA.set(2, 2, lona[2]); final LUDecomposition decomp = new LUDecomposition(mA); mY = decomp.solve(mB); } catch (Exception e) { System.out.printf("y1 = %d, y2 = %d, y3 = %d%n", ya[0], ya[1], ya[2]); Matrix mX = null; try { mX = decomp.solve(mB); } catch (Exception e) { System.out.printf("x1 = %d, x2 = %d, x3 = %d%n", xa[0], xa[1], xa[2]);
d = (new Matrix(H)).lu().solve(new Matrix(g, nparm)).getRowPackedCopy(); } catch (RuntimeException re) {
" Inf"); LUDecomposition LU = new LUDecomposition(M); Matrix L = LU.getL(); Matrix U = LU.getU(); int[] p = LU.getPivot(); Matrix R = L.times(U).minus(M.getMatrix(p,0,n-1)); double res = R.norm1()/(n*eps);