Updated Version 5.0! |
---|

Try it for free with our fully functional 60-day trial version. |

QuickStart Samples

# Root Bracketing Solvers QuickStart Sample (Visual Basic)

Illustrates the use of the root bracketing solvers for solving equations in one variable in Visual Basic.

C# code F# code IronPython code Back to QuickStart Samples

' The RootBracketingSolver and derived classes reside in the ' Extreme.Mathematics.EquationSolvers namespace. Imports Extreme.Mathematics.EquationSolvers ' Function delegates reside in the Extreme.Mathematics ' namespace. Imports Extreme.Mathematics.Algorithms Imports Extreme.Mathematics Namespace Extreme.Numerics.QuickStart.VB ' Illustrates the use of the root bracketing solvers ' in the Extreme.Mathematics.EquationSolvers namespace of the Extreme ' Optimization Numerical Libraries for .NET. Module RootBracketingSolvers Sub Main() ' Root bracketing solvers are used to solve ' non-linear equations in one variable. ' ' Root bracketing solvers start with an interval ' which is known to contain a root. This interval ' is made smaller and smaller in successive ' iterations until a certain tolerance is reached, ' or the maximum number of iterations has been ' exceeded. ' ' The properties and methods that give you control ' over the iteration are shared by all classes ' that implement iterative algorithms. ' ' Target function ' ' The function we are trying to solve must be ' provided as a Func(Of Double, Double). For more ' information about this delegate, see the ' Functions QuickStart sample. Dim f As Func(Of Double, Double) = AddressOf Math.Cos ' All root bracketing solvers inherit from ' RootBracketingSolver, an abstract base class. Dim solver As RootBracketingSolver ' ' Bisection method ' ' The bisection method halves the interval during ' each iteration. It is implemented by the ' BisectionSolver class. Console.WriteLine("BisectionSolver: cos(x) = 0 over [1,2]") solver = New BisectionSolver() solver.LowerBound = 1 solver.UpperBound = 2 solver.TargetFunction = f Dim result As Double = solver.Solve() ' The Status property indicates ' the result of running the algorithm. Console.WriteLine(" Result: {0}", _ solver.Status) ' The result is also available through the ' Result property. Console.WriteLine(" Solution: {0}", solver.Result) ' You can find out the estimated error of the result ' through the EstimatedError property: Console.WriteLine(" Estimated error: {0}", _ solver.EstimatedError) Console.WriteLine(" # iterations: {0}", _ solver.IterationsNeeded) ' ' Regula Falsi method ' ' The Regula Falsi method optimizes the selection ' of the next interval. Unfortunately, the ' optimization breaks down in some cases. ' Here is an example: Console.WriteLine("RegulaFalsiSolver: cos(x) = 0 over [1,2]") solver = New RegulaFalsiSolver() solver.LowerBound = 1 solver.UpperBound = 2 solver.MaxIterations = 1000 solver.TargetFunction = f result = solver.Solve() Console.WriteLine(" Result: {0}", _ solver.Status) Console.WriteLine(" Solution: {0}", solver.Result) Console.WriteLine(" Estimated error: {0}", _ solver.EstimatedError) Console.WriteLine(" # iterations: {0}", _ solver.IterationsNeeded) ' However, for sin(x) = 0, everything is fine: Console.WriteLine("RegulaFalsiSolver: sin(x) = 0 over [-0.5,1]") solver = New RegulaFalsiSolver() solver.LowerBound = -0.5 solver.UpperBound = 1 solver.TargetFunction = AddressOf Math.Sin result = solver.Solve() Console.WriteLine(" Result: {0}", _ solver.Status) Console.WriteLine(" Solution: {0}", solver.Result) Console.WriteLine(" Estimated error: {0}", _ solver.EstimatedError) Console.WriteLine(" # iterations: {0}", _ solver.IterationsNeeded) ' ' Dekker-Brent method ' ' The Dekker-Brent method combines the best of ' both worlds. It is the most robust and, on average, ' the fastest method. Console.WriteLine("DekkerBrentSolver: cos(x) = 0 over [1,2]") solver = New DekkerBrentSolver() solver.LowerBound = 1 solver.UpperBound = 2 solver.TargetFunction = f result = solver.Solve() Console.WriteLine(" Result: {0}", _ solver.Status) Console.WriteLine(" Solution: {0}", solver.Result) Console.WriteLine(" Estimated error: {0}", _ solver.EstimatedError) Console.WriteLine(" # iterations: {0}", _ solver.IterationsNeeded) ' ' Controlling the process ' Console.WriteLine("Same with modified parameters:") ' You can set the maximum # of iterations: ' If the solution cannot be found in time, the ' Status will return a value of ' IterationStatus.IterationLimitExceeded solver.MaxIterations = 20 ' You can specify how convergence is to be tested ' through the ConvergenceCriterion property: solver.ConvergenceCriterion = _ ConvergenceCriterion.WithinRelativeTolerance ' And, of course, you can set the absolute or ' relative tolerance. solver.RelativeTolerance = 0.00001 ' In this example, the absolute tolerance will be ' ignored. solver.AbsoluteTolerance = 1.0E-24 solver.LowerBound = 157081 solver.UpperBound = 157082 solver.TargetFunction = f result = solver.Solve() Console.WriteLine(" Result: {0}", _ solver.Status) Console.WriteLine(" Solution: {0}", solver.Result) ' The estimated error will be less than 0.157 Console.WriteLine(" Estimated error: {0}", _ solver.EstimatedError) Console.WriteLine(" # iterations: {0}", _ solver.IterationsNeeded) Console.Write("Press Enter key to exit...") Console.ReadLine() End Sub End Module End Namespace

Copyright Â© 2003-2014, Extreme Optimization. All rights reserved.

*Extreme Optimization,* *Complexity made simple*, *M#*, and *M Sharp* are trademarks of ExoAnalytics Inc.

*Microsoft*, *Visual C#, Visual Basic, Visual Studio*, *Visual Studio.NET*, and the *Optimized for Visual Studio* logo

are registered trademarks of Microsoft Corporation.