Solves the unconstrained minimization problem for an arbitrary nonlinear function. You must manually select the polymorphic instance to use.


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Inputs/Outputs

  • c1dstr.png X —

    X is an array of strings representing the x variables.

  • cstr.png f(X) —

    f(X) is the string representing the function of the x variables. The formulas can contain any number of valid variables.

  • c1ddbl.png start —

    start is a point in n dimension at which the optimization process starts.

  • cnclst.png stopping criteria —

    stopping criteria is the collection of conditions that terminate the optimization. If (function tolerance AND parameter tolerance AND gradient tolerance) OR max iterations OR max function calls then optimization terminates.

  • cdbl.png function tolerance —

    function tolerance is the relative change in function value and is defined as abs(current f – prev f)/(abs(curr f)+machine eps). If the relative change in the function value falls below function tolerance, the optimization terminates.

  • cdbl.png parameter tolerance —

    parameter tolerance is the relative change in parameter values and is defined as abs(current p – prev p)/(abs(curr p)+machine eps). If the relative change of all the parameter values falls below parameters tolerance, the optimization terminates.

  • ci32.png max iterations —

    max iterations is the largest number of iterations of the major loop of the optimization. If the number of major loop iterations exceeds max iterations, the optimization terminates.

  • ci32.png max function calls —

    max function calls is the largest number of objective function calls allowed before terminating the optimization process.

  • cdbl.png gradient tolerance —

    gradient tolerance is the 2–norm of the gradient. If the 2–norm of the gradient falls below gradient tolerance, the optimization terminates.

  • cdbl.png max time (sec) —

    max time (sec) is the maximum amount of time LabVIEW allows between the start and the end of the optimization process. The default is –1. –1 indicates never to time out.

  • cerrcodeclst.png error in (no error) —

    error in describes error conditions that occur before this node runs. This input provides standard error in functionality.

  • i1ddbl.png minimum —

    minimum is the determined local minimum in n dimension.

  • idbl.png f(minimum) —

    f(minimum) is the function value of f(X) at the determined minimum.

  • ii32.png number of function evaluations —

    number of function evaluations is the number of times the objective function was called in the optimization process.

  • ierrcodeclst.png error out —

    error out contains error information. This output provides standard error out functionality.

    • For functions that are smooth and have first and second derivatives defined, the Broyden Quasi-Newton algorithm typically converges the fastest. If you experience problems with convergence of the Broyden Quasi-Newton algorithm, the Conjugate Gradient algorithm might be able to solve the problem. The Downhill Simplex algorithm relies only on function evaluations and often is able to find a solution when the function is not smooth and the other algorithms fail to converge.

    Examples

    Refer to the following example files included with LabVIEW.

    • labview\examples\Mathematics\Optimization\Optimize Extended Rosenbrock.vi