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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
''' <summary>
''' Minimizes a function value using IPOPT solver.
''' </summary>
''' <param name="functionbody">f(x) where x is a vector of doubles, returns the value of the function.</param>
''' <param name="functiongradient">Optional. g(x) where x is a vector of doubles, returns the value of the gradient of the function with respect to each variable.</param>
''' <param name="vars">initial values for x</param>
''' <param name="lbounds">lower bounds for x</param>
''' <param name="ubounds">upper bounds for x</param>
''' <returns>vector of variables corresponding to the function's minimum value.</returns>
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
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