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tutorial.cpp (Start Here)

To be able to quickly start with a toolkit, it is often advantageous (not only for the impatient users), to look at some code examples first.

In this tutorial, we give a short example program which interpolates a bivariate function on a regular sparse grid. 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.

In the example, we create a two-dimensional regular sparse grid with level 3 (with grid points \(\vec{x}_j \in [0, 1]^2\)) using piecewise bilinear basis functions \(\varphi_j\colon [0, 1]^2 \to \mathbb{R}\). We then interpolate the function

\[ f\colon [0, 1]^2 \to \mathbb{R},\quad f(x_0, x_1) := 16 (x_0 - 1) x_0 (x_1 - 1) x_1 \]

with

\[ u\colon [0, 1]^2 \to \mathbb{R},\quad u(x_0, x_1) := \sum_{j=0}^{N-1} \alpha_j \varphi_j(x_0, x_1) \]

by calculating the coefficients \(\alpha_j\) such that \(u(\vec{x}_j) = f(\vec{x}_j)\) for all \(j\). This process is called hierarchization in sparse grid contexts; the \(\alpha_j\) are called (hierarchical) surpluses. Note that \(f\) vanishes at the boundary of the domain \([0, 1]^2\); therefore, we don't have to spend sparse grid points on the boundary. Finally, we evaluate the sparse grid function \(u\) at a point \(\vec{p} = (0.52, 0.73)\).

First, we have to include the SG++ headers. We can include the meta-header sgpp_base.hpp, which includes itself all headers from the base module, or we can include only those headers we need.

// include all SG++ base headers
// #include <sgpp_base.hpp>
// or, better, include only the ones needed
#include <iostream>

Before starting with the main function, the function \(f\), which we want to interpolate, is defined.

double f(double x0, double x1) {
return 16.0 * (x0 - 1) * x0 * (x1 - 1) * x1;
}
int main() {

First, we create a two-dimensional grid (type sgpp::base::Grid) with piecewise bilinear basis functions with the help of the factory method sgpp::base::Grid.createLinearGrid().

size_t dim = 2;
std::unique_ptr<sgpp::base::Grid> grid(sgpp::base::Grid::createLinearGrid(dim));

Then we obtain a reference to the grid's sgpp::base::GridStorage object which allows us, e.g., to access grid points, to obtain the dimensionality (which we print) and the number of grid points.

sgpp::base::GridStorage& gridStorage = grid->getStorage();
std::cout << "dimensionality: " << gridStorage.getDimension() << std::endl;

Now, we use a sgpp::base::GridGenerator to create a regular sparse grid of level 3. Thus, gridStorage.getSize() returns 17, the number of grid points of a two-dimensional regular sparse grid of level 3.

size_t level = 3;
grid->getGenerator().regular(level);
std::cout << "number of grid points: " << gridStorage.getSize() << std::endl;

We create an object of type sgpp::base::DataVector which is essentially a wrapper around a double array. The DataVector is initialized with as many entries as there are grid points. It serves as a coefficient vector for the sparse grid interpolant we want to construct. As the entries of a freshly created DataVector are not initialized, we set them to 0.0. (This is superfluous here as we initialize them in the next few lines anyway.)

sgpp::base::DataVector alpha(gridStorage.getSize());
alpha.setAll(0.0);
std::cout << "length of alpha vector: " << alpha.getSize() << std::endl;

The for loop iterates over all grid points: For each grid point gp, the corresponding coefficient \(\alpha_j\) is set to the function value at the grid point's coordinates which are obtained by getStandardCoordinate(dim). The current coefficient vector is then printed.

for (size_t i = 0; i < gridStorage.getSize(); i++) {
sgpp::base::GridPoint& gp = gridStorage.getPoint(i);
alpha[i] = f(gp.getStandardCoordinate(0), gp.getStandardCoordinate(1));
}
std::cout << "alpha before hierarchization: " << alpha.toString() << std::endl;

An object of sgpp::base::OperationHierarchisation is created and used to hierarchize the coefficient vector, which we print.

std::cout << "alpha after hierarchization: " << alpha.toString() << std::endl;

Finally, a second DataVector is created which is used as a point to evaluate the sparse grid function at. An object is obtained which provides an evaluation operation (of type sgpp::base::OperationEvaluation), and the sparse grid interpolant is evaluated at \(\vec{p}\), which is close to (but not exactly at) a grid point.

p[0] = 0.52;
p[1] = 0.73;
std::unique_ptr<sgpp::base::OperationEval> opEval(sgpp::op_factory::createOperationEval(*grid));
std::cout << "u(0.52, 0.73) = " << opEval->eval(alpha, p) << std::endl;
}

The example results in the following output:

dimensionality:         2
number of grid points:  17
length of alpha vector: 17
alpha before hierarchization: [1, 0.75, 0.75, 0.4375, 0.9375, 0.9375, 0.4375, 0.75, 0.75, 0.4375, 0.9375, 0.9375, 0.4375, 0.5625, 0.5625, 0.5625, 0.5625]
alpha after hierarchization:  [1, 0.25, 0.25, 0.0625, 0.0625, 0.0625, 0.0625, 0.25, 0.25, 0.0625, 0.0625, 0.0625, 0.0625, 0.0625, 0.0625, 0.0625, 0.0625]
u(0.52, 0.73) = 0.7696

It can be clearly seen that the surpluses decay with a factor of 1/4: On the first level, we obtain 1, on the second 1/4, and on the third 1/16 as surpluses.