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Imports System
Imports CenterSpace.NMath.Core
Namespace CenterSpace.NMath.Examples.VisualBasic
A .NET example in Visual Basic
Module NonlinearProgrammingExample
Example illustrating using the Module ActiveSetLineSearchSQP to solve a
NonLinear Programming (NLP) problem with linear constraints and variable
bounds.
Sub Main()
min -x0*x1*x2
0 <= x0 + 2*x1 + 2*x2 <= 72,
0 <= x0, x1, x2 <= 42
Dimensionality of the domain of the objective function.
Dim XDim As Integer = 3
Dim Objective As Func(Of DoubleVector, Double) = AddressOf Temp
Dim Problem As New NonlinearProgrammingProblem(XDim, Objective)
Add variable bounds.
For I = 0 To XDim - 1
Add a lower bound of 0 and upper bound of 42 for the ith variable.
Problem.AddBounds(I, 0.0, 42.0)
Next
Add the constraint 0 <= x0 + 2*x1 + 2*x2 <= 72. Note that this
is a linear constraint.
Problem.AddLinearConstraint(New DoubleVector(1.0, 2.0, 2.0), 0.0, 72)
Pick the point (1, 1, 1) as initial solution guess.
Dim x0 As New DoubleVector(3, 1.0)
Pick a tolerance for convergence. The iteration will stop when
either the magnitude of the predicted function value change or the
magnitude of the step direction is less than the specified tolerance.
Dim tolerance As Double = 0.0001
Dim solver As New ActiveSetLineSearchSQP(tolerance)
Dim Success As Boolean = solver.Solve(Problem, x0)
Console.WriteLine()
Console.WriteLine("Termination status = " & solver.SolverTerminationStatus.ToString())
Console.WriteLine("X = " & solver.OptimalX.ToString())
Console.WriteLine("f(x) = " & solver.OptimalObjectiveFunctionValue)
Console.WriteLine("Iterations = " & solver.Iterations)
Seems like it took quite a few iterations to converge. Note that the
solver computes a step direction and a step size at each iteration.
The step direction is computed by forming a quadratic programming
problem with linear constraints at each iteration whose solution
yields the step direction. The size of the step taken in this
direction is then chosen to decrease the function value and not
violate constraints. Since our constraints for this problem are linear
to begin with, we may try taking a larger step in order to converge
faster. Well use the ConstantSQPStepSize Module with a constant step
size of one and see what happens.
solver.SolverOptions.StepSizeCalculator = New ConstantSQPStepSize(1)
Success = solver.Solve(Problem, x0)
Console.WriteLine()
Console.WriteLine("Using a constant step size of 1:")
Console.WriteLine("Termination status = " & solver.SolverTerminationStatus.ToString())
Console.WriteLine("X = " & solver.OptimalX.ToString())
Console.WriteLine("f(x) = " & solver.OptimalObjectiveFunctionValue)
Console.WriteLine("Iterations = " & solver.Iterations)
As you can see the algorithm converges must faster with the constant
step size of one. In general if your constraints are all linear, or
very nearly linear, the constant step size calculator may yield
faster convergence.
Console.WriteLine()
Console.WriteLine("Press Enter Key")
Console.Read()
End Sub
Function Temp(ByVal x As DoubleVector) As Double
Return -x(0) * x(1) * x(2)
End Function
End Module
End Namespace
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