Public Class BFGSBMinimizer Public Property Tolerance As Double = 0.0001 Public Property MaxIterations As Integer = 1000 Public Property ReturnLowestObjFuncValue As Boolean = True Private _Iterations As Integer = 0 Private fxb As Func(Of Double(), Double) Private fxg As Func(Of Double(), Double()) Private _error As Double Private objval, objval0 As Double Public ReadOnly Property Iterations As Integer Get Return _Iterations End Get End Property Sub New() End Sub Public Shared Function FindRoots(functionbody As Func(Of Double(), Double), vars As Double(), maxits As Integer, tol As Double, Optional lbounds As Double() = Nothing, Optional ubounds As Double() = Nothing) As Double() Dim bfgsb As New BFGSBMinimizer bfgsb.Tolerance = tol bfgsb.MaxIterations = maxits Return bfgsb.Solve(functionbody, Nothing, vars, lbounds, ubounds) End Function ''' ''' Minimizes a function value using IPOPT solver. ''' ''' f(x) where x is a vector of doubles, returns the value of the function. ''' Optional. g(x) where x is a vector of doubles, returns the value of the gradient of the function with respect to each variable. ''' initial values for x ''' lower bounds for x ''' upper bounds for x ''' vector of variables corresponding to the function's minimum value. Public Function Solve(functionbody As Func(Of Double(), Double), functiongradient As Func(Of Double(), Double()), vars As Double(), Optional lbounds As Double() = Nothing, Optional ubounds As Double() = Nothing) As Double() _Iterations = 0 Dim obj As Double = 0.0# fxb = functionbody fxg = functiongradient If functiongradient Is Nothing Then fxg = Function(xv) Return FunctionGradientInternal(xv) End Function Else fxg = functiongradient End If If lbounds Is Nothing Then lbounds = vars.Clone() For i As Integer = 0 To lbounds.Length - 1 lbounds(i) = -1.0E+19 Next End If If ubounds Is Nothing Then ubounds = vars.Clone() For i As Integer = 0 To ubounds.Length - 1 ubounds(i) = 1.0E+19 Next End If Dim slv As New MathNet.Numerics.Optimization.BfgsBMinimizer(Tolerance, Tolerance, Tolerance, MaxIterations) Dim objf = MathNet.Numerics.Optimization.ObjectiveFunction.Gradient(Function(xvec) Return fxb.Invoke(xvec.ToArray()) End Function, Function(xvec) Return New MathNet.Numerics.LinearAlgebra.[Double].DenseVector(fxg.Invoke(xvec.ToArray())) End Function) Dim solution = slv.FindMinimum(objf, New MathNet.Numerics.LinearAlgebra.[Double].DenseVector(lbounds), New MathNet.Numerics.LinearAlgebra.[Double].DenseVector(ubounds), New MathNet.Numerics.LinearAlgebra.[Double].DenseVector(vars)) vars = solution.MinimizingPoint.ToArray() Return vars End Function Private Function FunctionGradientInternal(ByVal x() As Double) As Double() Dim epsilon As Double = 0.001 Dim f1, f2 As Double Dim g(x.Length - 1), x1(x.Length - 1), x2(x.Length - 1) As Double Dim j, k As Integer For j = 0 To x.Length - 1 For k = 0 To x.Length - 1 x1(k) = x(k) x2(k) = x(k) Next If x(j) <> 0.0# Then x1(j) = x(j) * (1.0# + epsilon) x2(j) = x(j) * (1.0# - epsilon) Else x1(j) = x(j) + epsilon x2(j) = x(j) - epsilon End If f1 = fxb.Invoke(x1) f2 = fxb.Invoke(x2) g(j) = (f2 - f1) / (x2(j) - x1(j)) Next Return g End Function End Class