Package org.apache.commons.math.analysis

Examples of org.apache.commons.math.analysis.MultivariateVectorialFunction

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      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();
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      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();
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      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 }
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            }
            return sum;
        }

        public MultivariateVectorialFunction gradient() {
            return new MultivariateVectorialFunction() {
                private static final long serialVersionUID = 2621997811350805819L;
                public double[] value(double[] point) {
                    return gradient(point);
                }
            };
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            return sum;

        }

        public MultivariateVectorialFunction gradient() {
            return new MultivariateVectorialFunction() {
                private static final long serialVersionUID = 3174909643301201710L;
                public double[] value(double[] point) {
                    return gradient(point);
                }
            };
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            }
            return sum;
        }

        public MultivariateVectorialFunction gradient() {
            return new MultivariateVectorialFunction() {
                private static final long serialVersionUID = 2621997811350805819L;
                public double[] value(double[] point) {
                    return gradient(point);
                }
            };
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            return sum;

        }

        public MultivariateVectorialFunction gradient() {
            return new MultivariateVectorialFunction() {
                public double[] value(double[] point) {
                    return gradient(point);
                }
            };
        }
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            }
            return sum;
        }

        public MultivariateVectorialFunction gradient() {
            return new MultivariateVectorialFunction() {
                private static final long serialVersionUID = 2621997811350805819L;
                public double[] value(double[] point) {
                    return gradient(point);
                }
            };
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            return sum;

        }

        public MultivariateVectorialFunction gradient() {
            return new MultivariateVectorialFunction() {
                private static final long serialVersionUID = 3174909643301201710L;
                public double[] value(double[] point) {
                    return gradient(point);
                }
            };
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      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();
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Related Classes of org.apache.commons.math.analysis.MultivariateVectorialFunction

  • org.apache.commons.math.optimization.direct.NelderMeadTest
  • org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizerTest$Circle
  • org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizerTest$LinearProblem
  • org.apache.commons.math.optimization.MultiStartDifferentiableMultivariateRealOptimizerTest$Circle
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