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TMtxOptimization Class

Interfaces the optimization routines.

Dew_Math_TMtxOptimization
Syntax
C#
Visual Basic
public class TMtxOptimization : TMtxComponent;

MtxVecTools.cs

The component can be used to find the minimum of function of several variables. 

 

How to use TMtxOptimization component? 

  • Drop a TMtxOptimization component on the form.
  • Define the number of variables and their initial values by accessing the VariableParameters vector.
  • Define any additional constant parameters by accessing ConstantParameters vector.
  • Define any additional constant pointer parameter by using the SetObjects method.
  • Define real function (must be of TRealFunction type).
  • Define optimization method by accessing the OptimizationMethod property.
  • Depending on optimization method you'll have to (optionally) define the gradient calculation

procedure (GradProcedure method) or gradient/Hessian matrix calculation procedure (GradHessProcedure method). If you don't specify the GradProcedure or GradHessProcedure then the numeric approximation will be used to calculate the gradient vector and Hessian matrix. In this case you must also specify which gradient aproximation method you will use - access the NumericGradMethod property.

  • Call the Recalculate method to find the minimum of function of several variables.

 

Results:

How to solve the optimization problem using TMtxOptimization component?

using Dew.Math; using Dew.Math.Units; namespace Dew.Examples { // define the real function to be minimized private double Banana(TVec pars, TVec consts, params object[] objConsts) { return 100*Math.Pow(pars[1]-Math.Pow(pars[0],2),2)+Math.Pow(1-pars[0],2); } private void Example(TMtxOptimization MtxOptim); { if (MtxOptim != null) { // define two variables and their initial values MtxOptim.VariableParameters.SetIt(false, new double[] {2,-1}); // use BFGS optimization method MtxOptim.OptimizationMethod = TOptMethod.optBFGS; // tolerance for MinValue and gradient calculation // additional note : since we did not define the GradProc, // the internal numerical gradient approximation will be used MtxOptim.Tolerance = 2.0e-6; MtxOptim.GradTolerance = 2.0e-6; // function to be minimized MtxOptim.RealFunction = Banana; // finally, calculate minimum MtxOptim.Recalculate(); } } }
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