Examples of com.opengamma.analytics.math.matrix.OGMatrixAlgebra.multiply()

Package com.opengamma.analytics.math.matrix

Class com.opengamma.analytics.math.matrix.OGMatrixAlgebra

Examples of com.opengamma.analytics.math.matrix.OGMatrixAlgebra.multiply()

com.opengamma.analytics.math.matrix.OGMatrixAlgebra.multiply()
{@inheritDoc}The following combinations of input matrices m1 and m2 are allowed:
- m1 = 2-D matrix, m2 = 2-D matrix, returns $\mathbf{C} = \mathbf{AB}$
- m1 = 2-D matrix, m2 = 1-D matrix, returns $\mathbf{C} = \mathbf{A}b$
- m1 = 1-D matrix, m2 = 2-D matrix, returns $\mathbf{C} = a^T\mathbf{B}$

    final DoubleMatrix2D fDmatrix = new DoubleMatrix2D(fD);
    // Derivative of abc with respect to forward
    final ColtMatrixAlgebra algebra = new ColtMatrixAlgebra();
    final DoubleMatrix2D fDInverse = algebra.getInverse(fDmatrix);
    final OGMatrixAlgebra algebraOG = new OGMatrixAlgebra();
    final DoubleMatrix1D derivativeF = (DoubleMatrix1D) algebraOG.multiply(fDInverse, pDFvector);
    return derivativeF.getData();
  }


  /**
   * Computes the derivative of the three fitting parameters with respect to the SABR parameters.

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    final ColtMatrixAlgebra algebra = new ColtMatrixAlgebra();
    final DoubleMatrix2D fDInverse = algebra.getInverse(fDmatrix);
    final OGMatrixAlgebra algebraOG = new OGMatrixAlgebra();
    for (int loopparam = 0; loopparam < 4; loopparam++) {
      final DoubleMatrix1D pDSABRvector = new DoubleMatrix1D(pDSABR[loopparam]);
      final DoubleMatrix1D derivativeSABR = (DoubleMatrix1D) algebraOG.multiply(fDInverse, pDSABRvector);
      result[loopparam] = derivativeSABR.getData();
    }
    return result;
  }

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    final LUDecompositionCommons decmp = new LUDecompositionCommons();
    final LUDecompositionResult decmpRes = decmp.evaluate(res.getCovariance());
    final DoubleMatrix2D invCovariance = decmpRes.solve(DoubleMatrixUtils.getIdentityMatrix2D(4));


    double z = ma.getInnerProduct(delta, ma.multiply(invCovariance, delta));
    z = Math.sqrt(z);


    assertTrue(z < 3.0);


    //     System.out.println("chiSqr: " + res.getChiSq());

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    final DoubleMatrix1D solution = new DoubleMatrix1D(new double[] {1.0, 1.0, 0.0, 0.0 });
    final NonLinearLeastSquare ls = new NonLinearLeastSquare();
    final DoubleMatrix2D res = ls.calInverseJacobian(SIGMA, FUNCTION, GRAD, solution);
    //  System.out.println("invese Jac: " + res);


    final DoubleMatrix1D deltaParms = (DoubleMatrix1D) ma.multiply(res, deltaY);
    // System.out.println("delta parms: " + deltaParms);


    final DoubleMatrix1D y = (DoubleMatrix1D) ma.add(Y, deltaY);


    final LeastSquareResults lsRes = ls.solve(X, y, SIGMA, PARAM_FUNCTION, PARAM_GRAD, solution);

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