# Intermediate course: Optimization

For many tasks in Jstacs, especially for numerical parameter learning, we need numerical optimization techniques.

We start the description of numerical optimization in Jstacs with the definition of a function that depends on parameters to be optimized. The most simple way to define such a function is to extend the abstract class NumericalDifferentiableFunction. In this abstract class, the gradient of the function is approximated by evaluating the function in an epsilon neightborhood around the current parameter values. Hence, a sub-class of NumericalDifferentiableFunction must only implement two methods. The first returns the number of parameters, and the second returns the function value for given parameter values.

Let us assume that we want to optimize, i.e., minimize, a function $f(x_1,x_2)=x_1^2 + x_2^2$. This functions depends on two parameters. Hence, we implement

public int getDimensionOfScope() {
return 2;
}

And we implement the method returning the function value as

public double evaluateFunction( double[] x ) throws DimensionException, EvaluationException {
return x[0]*x[0] + x[1]*x[1];
}

Now we are set to start a numerical optimization.

However, often we can derive the gradient analytically, which often yield a more efficient numerical optimization. Hence, the abstract class DifferentiableFunction allows to implement the computation of the gradient explicitly. To this end, we extend DifferentiableFunction and implement an additional method that computes the gradient:

public double[] evaluateGradientOfFunction( double[] x ) throws DimensionException, EvaluationException {
return new double[]{2.0*x[0], 2.0*x[1]};
}

Now we can start a numerical optimization. To this end, we need to specify a TerminationCondition that determines when to stop the iterations of the optimization. For example, such a TerminationCondition may stop the optimization, if the difference of successive function evaluations does not exceed a given threshold:

AbstractTerminationCondition tc = new SmallDifferenceOfFunctionEvaluationsCondition( 1E-6 );

In this example, this threshold is set to 10 − 6. Another option would be to stop after 100 iterations or if the gradient becomes small:

AbstractTerminationCondition tc2 = new IterationCondition(100);
AbstractTerminationCondition tc3 = new SmallGradientConditon( 1E-6 );

And we may also combine several TerminationCondition in a CombinedCondition:

TerminationCondition combined = new CombinedCondition( 2, tc, tc3 );

The first parameter (2) specifies that the CombinedCondition only allows to continue to the next iteration if both of the supplied TerminationCondition s do so.

For the numerical optimization, we create initial parameters and start the optimization:

double[] parameters = new double[df.getDimensionOfScope()];
Optimizer.optimize( Optimizer.QUASI_NEWTON_BFGS, df, parameters, combined, 1E-6, new ConstantStartDistance( 1E-4 ), System.out );

The arguments of this static method have the following meaning: The first argument defines the technique for the optimization. In the example, we set this to the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno. As an alternative, Jstacs offers other quasi-Newton method including limited-memory variants, different conjugate gradients approaches, and steepest descent. The second argument is the DifferentiableFunction, the third are the initial parameter values, the fourth is the TerminationCondition, and the fifth is the threshold on the difference of function values during the line search. Jstacs uses Brent's method, which is a combination of quadratic interpolation and golden ratio, for the line search. The sixth argument is the initial step size during the line search, and the last argument is an `OutputStream` to which output of the optimization is written. Here, we specify `System.out`. If this argument is `null`, output is suppressed.

After the optimization has finished, the supplied parameter array (`parameters` in the example) contains the optimal parameter values.