Examples of org.apache.commons.math.optimization.LeastSquaresConverter

Package org.apache.commons.math.optimization

Examples of org.apache.commons.math.optimization.LeastSquaresConverter

org.apache.commons.math.optimization.LeastSquaresConverter
This class converts {@link MultivariateVectorialFunction vectorialobjective functions} to {@link MultivariateRealFunction scalar objective functions}when the goal is to minimize them.
This class is mostly used when the vectorial objective function represents a theoretical result computed from a point set applied to a model and the models point must be adjusted to fit the theoretical result to some reference observations. The observations may be obtained for example from physical measurements whether the model is built from theoretical considerations.

This class computes a possibly weighted squared sum of the residuals, which is a scalar value. The residuals are the difference between the theoretical model (i.e. the output of the vectorial objective function) and the observations. The class implements the {@link MultivariateRealFunction} interface and can therefore beminimized by any optimizer supporting scalar objectives functions.This is one way to perform a least square estimation. There are other ways to do this without using this converter, as some optimization algorithms directly support vectorial objective functions.

This class support combination of residuals with or without weights and correlations.
@see MultivariateRealFunction @see MultivariateVectorialFunction @version $Revision: 1070725 $ $Date: 2011-02-15 02:31:12 +0100 (mar. 15 févr. 2011) $ @since 2.0

      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();

View Full Code Here

      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();

View Full Code Here

      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 }

View Full Code Here

      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();

View Full Code Here

      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();

View Full Code Here

      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 }

View Full Code Here

      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();

View Full Code Here

      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();

View Full Code Here

      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 }

View Full Code Here

TOP

Related Classes of org.apache.commons.math.optimization.LeastSquaresConverter

org.apache.commons.math.optimization.direct.NelderMeadTest

org.apache.commons.math.FunctionEvaluationException

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.