Method Detail

• create

  public static LeastSquaresProblem create(MultivariateJacobianFunction model,
                           RealVector observed,
                           RealVector start,
                           RealMatrix weight,
                           ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
                           int maxEvaluations,
                           int maxIterations,
                           boolean lazyEvaluation,
                           ParameterValidator paramValidator)

  Create a LeastSquaresProblem from the given elements. There will be no weights applied (unit weights).

  Parameters:

  model - the model function. Produces the computed values.

  observed - the observed (target) values

  start - the initial guess.

  weight - the weight matrix

  checker - convergence checker

  maxEvaluations - the maximum number of times to evaluate the model

  maxIterations - the maximum number to times to iterate in the algorithm

  lazyEvaluation - Whether the call to LeastSquaresProblem.evaluate(RealVector) will defer the evaluation until access to the value is requested.

  paramValidator - Model parameters validator.

  Returns:

  the specified General Least Squares problem.

  Since:

  3.4

• create

  public static LeastSquaresProblem create(MultivariateJacobianFunction model,
                           RealVector observed,
                           RealVector start,
                           ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
                           int maxEvaluations,
                           int maxIterations)

  Create a LeastSquaresProblem from the given elements. There will be no weights applied (unit weights).

  Parameters:

  model - the model function. Produces the computed values.

  observed - the observed (target) values

  start - the initial guess.

  checker - convergence checker

  maxEvaluations - the maximum number of times to evaluate the model

  maxIterations - the maximum number to times to iterate in the algorithm

  Returns:

  the specified General Least Squares problem.

• create

  public static LeastSquaresProblem create(MultivariateJacobianFunction model,
                           RealVector observed,
                           RealVector start,
                           RealMatrix weight,
                           ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
                           int maxEvaluations,
                           int maxIterations)

  Create a LeastSquaresProblem from the given elements.

  Parameters:

  model - the model function. Produces the computed values.

  observed - the observed (target) values

  start - the initial guess.

  weight - the weight matrix

  checker - convergence checker

  maxEvaluations - the maximum number of times to evaluate the model

  maxIterations - the maximum number to times to iterate in the algorithm

  Returns:

  the specified General Least Squares problem.

• create

  public static LeastSquaresProblem create(MultivariateVectorFunction model,
                           MultivariateMatrixFunction jacobian,
                           double[] observed,
                           double[] start,
                           RealMatrix weight,
                           ConvergenceChecker<LeastSquaresProblem.Evaluation> checker,
                           int maxEvaluations,
                           int maxIterations)

  Create a LeastSquaresProblem from the given elements.

  This factory method is provided for continuity with previous interfaces. Newer applications should use create(MultivariateJacobianFunction, RealVector, RealVector, ConvergenceChecker, int, int), or create(MultivariateJacobianFunction, RealVector, RealVector, RealMatrix, ConvergenceChecker, int, int).

  Parameters:

  model - the model function. Produces the computed values.

  jacobian - the jacobian of the model with respect to the parameters

  observed - the observed (target) values

  start - the initial guess.

  weight - the weight matrix

  checker - convergence checker

  maxEvaluations - the maximum number of times to evaluate the model

  maxIterations - the maximum number to times to iterate in the algorithm

  Returns:

  the specified General Least Squares problem.

• weightMatrix

  public static LeastSquaresProblem weightMatrix(LeastSquaresProblem problem,
                                 RealMatrix weights)

  Apply a dense weight matrix to the LeastSquaresProblem.

  Parameters:

  problem - the unweighted problem

  weights - the matrix of weights

  Returns:

  a new LeastSquaresProblem with the weights applied. The original problem is not modified.

• weightDiagonal

  public static LeastSquaresProblem weightDiagonal(LeastSquaresProblem problem,
                                   RealVector weights)

  Apply a diagonal weight matrix to the LeastSquaresProblem.

  Parameters:

  problem - the unweighted problem

  weights - the diagonal of the weight matrix

  Returns:

  a new LeastSquaresProblem with the weights applied. The original problem is not modified.

• countEvaluations

  public static LeastSquaresProblem countEvaluations(LeastSquaresProblem problem,
                                     Incrementor counter)

  Count the evaluations of a particular problem. The counter will be incremented every time LeastSquaresProblem.evaluate(RealVector) is called on the returned problem.

  Parameters:

  problem - the problem to track.

  counter - the counter to increment.

  Returns:

  a least squares problem that tracks evaluations

• evaluationChecker

  public static ConvergenceChecker<LeastSquaresProblem.Evaluation> evaluationChecker(ConvergenceChecker<PointVectorValuePair> checker)

  View a convergence checker specified for a PointVectorValuePair as one specified for an LeastSquaresProblem.Evaluation.

  Parameters:

  checker - the convergence checker to adapt.

  Returns:

  a convergence checker that delegates to checker.

• model

  public static MultivariateJacobianFunction model(MultivariateVectorFunction value,
                                   MultivariateMatrixFunction jacobian)

  Combine a MultivariateVectorFunction with a MultivariateMatrixFunction to produce a MultivariateJacobianFunction.

  Parameters:

  value - the vector value function

  jacobian - the Jacobian function

  Returns:

  a function that computes both at the same time