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On this page, we look at an example application of the sgpp::optimization module.

Identical versions of the example are given in all languages currently supported by SG++: C++, Python, Java, and MATLAB.

For instructions on how to compile and run the example, please see Installation and Usage.

The example interpolates a bivariate test function like the tutorial.cpp (Start Here) example. However, we use B-splines here instead to obtain a smoother interpolant. The resulting sparse grid function is then minimized with the method of steepest descent. For comparison, we also minimize the objective function with Nelder-Mead's method.

The example uses the following external class (stored in The function \(f\colon [0, 1]^d \to \mathbb{R}\) to be minimized is called objective function and has to derive from sgpp.OptScalarFunction. In the constructor, we give the dimensionality of the domain (in this case \(d = 2\)). The eval method evaluates the objective function and returns the function value \(f(\vec{x})\) for a given point \(\vec{x} \in [0, 1]^d\).

// Copyright (C) 2008-today The SG++ project
// This file is part of the SG++ project. For conditions of distribution and
// use, please see the copyright notice provided with SG++ or at
public class ExampleFunction extends sgpp.OptScalarFunction {
public ExampleFunction() {
public double eval(sgpp.DataVector x) {
if ((x.get(0) >= 0.0) && (x.get(0) <= 1.0) &&
(x.get(1) >= 0.0) && (x.get(1) <= 1.0)) {
// minimum is f(x) = -2 for x[0] = 3*pi/16, x[1] = 3*pi/14
return Math.sin(8.0 * x.get(0)) + Math.sin(7.0 * x.get(1));
} else {
public void clone(sgpp.SWIGTYPE_p_std__unique_ptrT_sgpp__optimization__ScalarFunction_t clone) {

The actual example looks follows.

public class optimization {
static void printLine() {
System.out.println("----------------------------------------" +
static String numToStr(double number) {
return new java.text.DecimalFormat("#.#####").format(number);
public static void main(String[] args) {

At the beginning of the program, we have to load the shared library object file. We can do so by using java.lang.System.load or sgpp.LoadJSGPPLib.loadJSGPPLib. Also, we disable OpenMP within jsgpp since it interferes with SWIG's director feature.

System.out.println("sgpp::optimization example program started.\n");
// increase verbosity of the output

Here, we set define some parameters: objective function, dimensionality, B-spline degree, maximal number of grid points, and adaptivity.

// objective function
ExampleFunction f = new ExampleFunction();
// dimension of domain
final long d = f.getNumberOfParameters();
// B-spline degree
final long p = 3;
// maximal number of grid points
final long N = 30;
// adaptivity of grid generation
final double gamma = 0.95;

First, we define a grid with modified B-spline basis functions and an iterative grid generator, which can generate the grid adaptively.

sgpp.Grid grid = sgpp.Grid.createModBsplineGrid(d, p);
sgpp.OptIterativeGridGeneratorRitterNovak gridGen =
new sgpp.OptIterativeGridGeneratorRitterNovak(f, grid, N, gamma);

With the iterative grid generator, we generate adaptively a sparse grid.

System.out.println("Generating grid...\n");
if (!gridGen.generate()) {
System.out.println("Grid generation failed, exiting.");

Then, we hierarchize the function values to get hierarchical B-spline coefficients of the B-spline sparse grid interpolant \(\tilde{f}\colon [0, 1]^d \to \mathbb{R}\).

final sgpp.DataVector functionValues = gridGen.getFunctionValues();
sgpp.DataVector coeffs = new sgpp.DataVector(functionValues.getSize());
sgpp.OptHierarchisationSLE hierSLE = new sgpp.OptHierarchisationSLE(grid);
sgpp.OptAutoSLESolver sleSolver = new sgpp.OptAutoSLESolver();
// solve linear system
if (!sleSolver.solve(hierSLE, gridGen.getFunctionValues(), coeffs)) {
System.out.println("Solving failed, exiting.");

We define the interpolant \(\tilde{f}\) and its gradient \(\nabla\tilde{f}\) for use with the gradient method (steepest descent). Of course, one can also use other optimization algorithms from sgpp::optimization::optimizer.

System.out.println("Optimizing smooth interpolant...\n");
sgpp.OptInterpolantScalarFunction ft =
new sgpp.OptInterpolantScalarFunction(grid, coeffs);
sgpp.OptInterpolantScalarFunctionGradient ftGradient =
new sgpp.OptInterpolantScalarFunctionGradient(grid, coeffs);
sgpp.OptGradientDescent gradientDescent =
new sgpp.OptGradientDescent(ft, ftGradient);
sgpp.DataVector x0 = new sgpp.DataVector(d);
double fX0;
double ftX0;

The gradient method needs a starting point. We use a point of our adaptively generated sparse grid as starting point. More specifically, we use the point with the smallest (most promising) function value and save it in x0.

sgpp.GridStorage gridStorage = grid.getStorage();
// index of grid point with minimal function value
int x0Index = 0;
fX0 = functionValues.get(0);
for (int i = 1; i < functionValues.getSize(); i++) {
if (functionValues.get(i) < fX0) {
fX0 = functionValues.get(i);
x0Index = i;
x0 = gridStorage.getCoordinates(gridStorage.getPoint(x0Index));
ftX0 = ft.eval(x0);
System.out.println("x0 = " + x0);
System.out.println("f(x0) = " + numToStr(fX0) +
", ft(x0) = " + numToStr(ftX0) + "\n");

We apply the gradient method and print the results.

sgpp.DataVector xOpt = gradientDescent.getOptimalPoint();
final double ftXOpt = gradientDescent.getOptimalValue();
final double fXOpt = f.eval(xOpt);
System.out.println("\nxOpt = " + xOpt);
System.out.println("f(xOpt) = " + numToStr(fXOpt) +
", ft(xOpt) = " + numToStr(ftXOpt) + "\n");

For comparison, we apply the classical gradient-free Nelder-Mead method directly to the objective function \(f\).

System.out.println("Optimizing objective function (for comparison)...\n");
sgpp.OptNelderMead nelderMead = new sgpp.OptNelderMead(f, 1000);
sgpp.DataVector xOptNM = nelderMead.getOptimalPoint();
final double fXOptNM = nelderMead.getOptimalValue();
final double ftXOptNM = ft.eval(xOptNM);
System.out.println("\nxOptNM = " + xOptNM);
System.out.println("f(xOptNM) = " + numToStr(fXOptNM) +
", ft(xOptNM) = " + numToStr(ftXOptNM) + "\n");
System.out.println("\nsgpp::optimization example program terminated.");
} // end of class

The example program outputs the following results:

sgpp::optimization example program started.

Generating grid...

Adaptive grid generation (Ritter-Novak)...
    100.0% (N = 29, k = 3)
Done in 3ms.

Solving linear system (automatic method)...
    estimated nnz ratio: 59.8% 
    Solving linear system (Armadillo)...
        constructing matrix (100.0%)
        nnz ratio: 58.0%
        solving with Armadillo
    Done in 0ms.
Done in 1ms.
Optimizing smooth interpolant...

x0 = [0.625, 0.75]
f(x0) = -1.81786, ft(x0) = -1.81786

Optimizing (gradient method)...
    9 steps, f(x) = -2.000780
Done in 1ms.

xOpt = [0.589526, 0.673268]
f(xOpt) = -1.99999, ft(xOpt) = -2.00078

Optimizing objective function (for comparison)...

Optimizing (Nelder-Mead)...
    280 steps, f(x) = -2.000000
Done in 2ms.

xOptNM = [0.589049, 0.673198]
f(xOptNM) = -2, ft(xOptNM) = -2.00077


sgpp::optimization example program terminated.

We see that both the gradient-based optimization of the smooth sparse grid interpolant and the gradient-free optimization of the objective function find reasonable approximations of the minimum, which lies at \((3\pi/16, 3\pi/14) \approx (0.58904862, 0.67319843)\).