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How to: Use Constrained Non-Linear Programming Using the Nelder-Mead Solver

Solver Foundation 3.0

You can use the Nelder-Mead solver (NelderMeadSolver) for some types of constrained non-linear programming. For example, the Nelder-Mead solver can find the local optimum of a general variable constrained objective function.

One example of this technique is to use the Nelder-Mead (NM) solver to find the global minimum of the box-constrained two-dimensional Rosenbrock function created by mathematician Howard Rosenbrock in 1960. The following example uses a variant of the Nelder-Mead method to search for a local optimum by iteratively changing variable values over a range of possible solutions. The solver stops computation when the difference in objective values is less than ToleranceDifference.

To solve a 2-D Rosenbrock function using the Nelder-Mead solver

  1. Create a Console Application named Rosenbrock.

  2. Add a reference to Microsoft Solver Foundation on the .NET tab.

  3. Add the following Imports or using statements to the top of the Program code file.

    using Microsoft.SolverFoundation.Common;
    using Microsoft.SolverFoundation.Solvers;
  4. In the Main method, define initial values, lower bounds and upper bounds for the output variables by typing the following code:

    double[] xInitial = new double[] { 1, 1 };
    double[] xLower = new double[] { -1, -1 };
    double[] xUpper = new double[] { 1, 1 };
  5. Call the NelderMeadSolver and supply the Rosenbrock function, initial values, and bounds by typing the following code:

    var solution = NelderMeadSolver.Solve(
    x => (100 * Math.Pow(x[1] - Math.Pow(x[0], 2), 2)) + Math.Pow(1 - x[0], 2), xInitial, xLower, xUpper);
  6. Print the results by typing the following code.

    Console.WriteLine("solution = {0}", solution.GetSolutionValue(0));
    Console.WriteLine("x = {0}", solution.GetValue(1));
    Console.WriteLine("y = {0}", solution.GetValue(2));
  7. Press F5 to build and run the code.

    The command window shows the following results.


    solution = 0

    x = 1

    y = 1

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