Examples of org.apache.commons.math.linear.Array2DRowRealMatrix

org.apache.commons.math.linear.Array2DRowRealMatrix
ath.gatech.edu/~bourbaki/math2601/Web-notes/2num.pdf"> LU decomposition to support linear system solution and inverse.
The LU decomposition is performed as needed, to support the following operations:
- solve
- isSingular
- getDeterminant
- inverse
Usage notes:
- The LU decomposition is cached and reused on subsequent calls. If data are modified via references to the underlying array obtained using getDataRef(), then the stored LU decomposition will not be discarded. In this case, you need to explicitly invoke LUDecompose() to recompute the decomposition before using any of the methods above.
- As specified in the {@link RealMatrix} interface, matrix element indexingis 0-based -- e.g., getEntry(0, 0) returns the element in the first row, first column of the matrix.
@version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $

  @Test
  public void testLeastSquares2()
  throws FunctionEvaluationException, ConvergenceException {


      final RealMatrix factors =
          new Array2DRowRealMatrix(new double[][] {
              { 1.0, 0.0 },
              { 0.0, 1.0 }
          }, false);
      LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 }, new double[] { 10.0, 0.1 });
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);

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  @Test
  public void testLeastSquares3()
  throws FunctionEvaluationException, ConvergenceException {


      final RealMatrix factors =
          new Array2DRowRealMatrix(new double[][] {
              { 1.0, 0.0 },
              { 0.0, 1.0 }
          }, false);
      LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 }, new Array2DRowRealMatrix(new double [][] {
          { 1.0, 1.2 }, { 1.2, 2.0 }
      }));
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);

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  @Test
  public void testLeastSquares1()
  throws FunctionEvaluationException, ConvergenceException {


      final RealMatrix factors =
          new Array2DRowRealMatrix(new double[][] {
              { 1.0, 0.0 },
              { 0.0, 1.0 }
          }, false);
      LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 });
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);

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  @Test
  public void testLeastSquares2()
  throws FunctionEvaluationException, ConvergenceException {


      final RealMatrix factors =
          new Array2DRowRealMatrix(new double[][] {
              { 1.0, 0.0 },
              { 0.0, 1.0 }
          }, false);
      LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 }, new double[] { 10.0, 0.1 });
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);

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  @Test
  public void testLeastSquares3()
  throws FunctionEvaluationException, ConvergenceException {


      final RealMatrix factors =
          new Array2DRowRealMatrix(new double[][] {
              { 1.0, 0.0 },
              { 0.0, 1.0 }
          }, false);
      LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 }, new Array2DRowRealMatrix(new double [][] {
          { 1.0, 1.2 }, { 1.2, 2.0 }
      }));
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);

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            x[i][0] = 1.0d;
            for (int j = 1; j < nvars + 1; j++) {
                x[i][j] = data[pointer++];
            }
        }
        this.X = new Array2DRowRealMatrix(x);
        this.Y = new ArrayRealVector(y);
    }

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     * Loads new x sample data, overriding any previous sample
     * 
     * @param x the [n,k] array representing the x sample
     */
    protected void newXSampleData(double[][] x) {
        this.X = new Array2DRowRealMatrix(x);
    }

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                // update Nordsieck vector
                final double[] predictedScaled = new double[y0.length];
                for (int j = 0; j < y0.length; ++j) {
                    predictedScaled[j] = stepSize * yDot[j];
                }
                final Array2DRowRealMatrix nordsieckTmp = updateHighOrderDerivativesPhase1(nordsieck);
                updateHighOrderDerivativesPhase2(scaled, predictedScaled, nordsieckTmp);


                // apply correction (C in the PECE sequence)
                error = nordsieckTmp.walkInOptimizedOrder(new Corrector(y, predictedScaled, yTmp));


                if (error <= 1.0) {


                    // evaluate a final estimate of the derivative (second E in the PECE sequence)
                    computeDerivatives(stepEnd, yTmp, yDot);

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            final double[] msI = multistep[i];
            for (int j = 0; j < first.length; ++j) {
                msI[j] -= first[j];
            }
        }
        return initialization.multiply(new Array2DRowRealMatrix(multistep, false));
    }

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                    // update Nordsieck vector
                    final double[] predictedScaled = new double[y0.length];
                    for (int j = 0; j < y0.length; ++j) {
                        predictedScaled[j] = stepSize * yDot[j];
                    }
                    final Array2DRowRealMatrix nordsieckTmp = updateHighOrderDerivativesPhase1(nordsieck);
                    updateHighOrderDerivativesPhase2(scaled, predictedScaled, nordsieckTmp);


                    // discrete events handling
                    interpolatorTmp.reinitialize(stepEnd, stepSize, predictedScaled, nordsieckTmp);
                    interpolatorTmp.storeTime(stepStart);

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