Package org.ddogleg.optimization

Interface UnconstrainedMinimization

All Superinterfaces:
IterativeOptimization, Serializable
All Known Implementing Classes:
QuasiNewtonBFGS_to_UnconstrainedMinimization, UnconMinTrustRegionBFGS_F64
public interface UnconstrainedMinimization extends IterativeOptimization

Optimization algorithm which seeks to minimize F(X) ∈ ℜ and X ∈ ℜN

Two convergence thresholds are specified, f-test and g-test. The f-test is a relative convergence test based on the function's value and is designed to test to see when it is near the optimal solution. G-Test is an absolute test based on the gradient's norm,

F-Test: ftol ≤ 1 - f(x+p)/f(x)
G-Test: gtol ≤ ||g(x)||inf
An absolute f-test can be done by checking the value of getFunctionValue() in each iteration.

NOTE: If you need to modify the optimization parameters this can be done inside the 'function'.

  • Method Summary

    Modifier and Type
    Method
    Description
    double
    getFunctionValue()
    Returns the value of the objective function being evaluated at the current parameters value.
    double[]
    getParameters()
    After each iteration this function can be called to get the current best set of parameters.
    void
    initialize(double[] initial, double ftol, double gtol)
    Specify the initial set of parameters from which to start from.
    void
    setFunction(FunctionNtoS function, FunctionNtoN gradient, double minFunctionValue)
    Specifies the function being optimized.

    Methods inherited from interface org.ddogleg.optimization.IterativeOptimization

    isConverged, isUpdated, iterate, setVerbose

  • Method Details

    • setFunction

      void setFunction(FunctionNtoS function, FunctionNtoN gradient, double minFunctionValue)
      Specifies the function being optimized. A numerical Jacobian will be computed if null is passed in.
      Parameters:
      function - Function being optimized.
      gradient - Partial derivative for each input in the function. If null a numerical gradient will be computed.
      minFunctionValue - Minimum possible value that 'function' can have. E.g. for least squares problems this value should be set to zero.

    • initialize

      void initialize(double[] initial, double ftol, double gtol)
      Specify the initial set of parameters from which to start from. Call after setFunction(org.ddogleg.optimization.functions.FunctionNtoS, org.ddogleg.optimization.functions.FunctionNtoN, double) has been called.
      Parameters:
      initial - Initial parameters or guess.
      ftol - Relative convergence test based on function value. 0 disables test. 0 ≤ ftol < 1, Try 1e-12
      gtol - Absolute convergence test based on gradient. 0 disables test. 0 ≤ gtol. Try 1e-12

    • getParameters

      double[] getParameters()
      After each iteration this function can be called to get the current best set of parameters.

    • getFunctionValue

      double getFunctionValue()
      Returns the value of the objective function being evaluated at the current parameters value. If not supported then an exception is thrown.
      Returns:
      Objective function's value.