|MultiFunctionMathFindLocalMinimum Method (FuncIListDouble, Double, IListDouble, EvaluationSettings)|
public static MultiExtremum FindLocalMinimum( Func<IList<double>, double> function, IList<double> start, EvaluationSettings settings )
Public Shared Function FindLocalMinimum ( function As Func(Of IList(Of Double), Double), start As IList(Of Double), settings As EvaluationSettings ) As MultiExtremum
public: static MultiExtremum^ FindLocalMinimum( Func<IList<double>^, double>^ function, IList<double>^ start, EvaluationSettings^ settings )
static member FindLocalMinimum : function : Func<IList<float>, float> * start : IList<float> * settings : EvaluationSettings -> MultiExtremum
The Hessian (matrix of second derivatives) returned with the minimum is an approximation that is constructed in the course of search. It should be considered a crude approximation, and may not even be that if the minimum is highly non-quadratic.
If you have a constrained minimization problem, require a high-precision solution, and do not have a good initial guess, consider first feeding your constrained problem into FindGlobalMinimum(FuncIListDouble, Double, IListInterval, EvaluationSettings), which supports constraints but gives relatively lower precision solutions, then feeding the result of that method into this method, which finds relatively precise solutions but does not support constraints.