Datasets:

introvoyz041
/

Dwsim

﻿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

	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