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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.

First, we include all the necessary headers, including those of the sgpp::base and sgpp::optimization module.

#include <sgpp_base.hpp>
#include <iostream>
#include <iterator>
#include <algorithm>

The function \(f\colon [0, 1]^d \to \mathbb{R}\) to be minimized is called objective function and has to derive from sgpp::optimization::ScalarFunction. 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 \(\vec{x} \in [0, 1]^d\). The clone method returns a std::unique_ptr to a clone of the object and is used for parallelization (in case eval is not thread-safe).

class ExampleFunction : public sgpp::optimization::ScalarFunction {
ExampleFunction() : sgpp::optimization::ScalarFunction(2) {
double eval(const sgpp::base::DataVector& x) {
// minimum is f(x) = -2 for x[0] = 3*pi/16, x[1] = 3*pi/14
return std::sin(8.0 * x[0]) + std::sin(7.0 * x[1]);
virtual void clone(
std::unique_ptr<sgpp::optimization::ScalarFunction>& clone) const {
clone = std::unique_ptr<sgpp::optimization::ScalarFunction>(
new ExampleFunction(*this));

Now, we can start with the main function.

void printLine() {
std::cout << "----------------------------------------"
int main(int argc, const char* argv[]) {
std::cout << "sgpp::optimization example program started.\n\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;
// dimension of domain
const size_t d = f.getNumberOfParameters();
// B-spline degree
const size_t p = 3;
// maximal number of grid points
const size_t N = 30;
// adaptivity of grid generation
const 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.

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

std::cout << "Generating grid...\n\n";
if (!gridGen.generate()) {
std::cout << "Grid generation failed, exiting.\n";
return 1;

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

std::cout << "Hierarchizing...\n\n";
sgpp::base::DataVector functionValues(gridGen.getFunctionValues());
sgpp::base::DataVector coeffs(functionValues.getSize());
// solve linear system
if (!sleSolver.solve(hierSLE, functionValues, coeffs)) {
std::cout << "Solving failed, exiting.\n";
return 1;

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.

std::cout << "Optimizing smooth interpolant...\n\n";
sgpp::optimization::optimizer::GradientDescent gradientDescent(ft, ftGradient);
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::base::GridStorage& gridStorage = grid.getStorage();
// index of grid point with minimal function value
size_t x0Index = std::distance(
functionValues.getPointer() +
x0 = gridStorage.getCoordinates(gridStorage[x0Index]);
fX0 = functionValues[x0Index];
ftX0 = ft.eval(x0);
std::cout << "x0 = " << x0.toString() << "\n";
std::cout << "f(x0) = " << fX0 << ", ft(x0) = " << ftX0 << "\n\n";

We apply the gradient method and print the results.

const sgpp::base::DataVector& xOpt = gradientDescent.getOptimalPoint();
const double ftXOpt = gradientDescent.getOptimalValue();
const double fXOpt = f.eval(xOpt);
std::cout << "\nxOpt = " << xOpt.toString() << "\n";
std::cout << "f(xOpt) = " << fXOpt << ", ft(xOpt) = " << ftXOpt << "\n\n";

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

std::cout << "Optimizing objective function (for comparison)...\n\n";
sgpp::base::DataVector xOptNM = nelderMead.getOptimalPoint();
const double fXOptNM = nelderMead.getOptimalValue();
const double ftXOptNM = ft.eval(xOptNM);
std::cout << "\nnxOptNM = " << xOptNM.toString() << "\n";
std::cout << "f(xOptNM) = " << fXOptNM <<
", ft(xOptNM) = " << ftXOptNM << "\n\n";
std::cout << "\nsgpp::optimization example program terminated.\n";
return 0;

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 \((3\pi/16, 3\pi/14) \approx (0.58904862, 0.67319843)\).