SG++
optimization.m

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

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

Note that the MATLAB example differs from the other languages as we do not optimize a custom objective function, but a built-in one. This is because the director feature in SWIG-matlab is still buggy. (In theory, it is possible to define a custom MATLAB class derived from sgpp.OptScalarFunction, but some SG++ functions then crash with "Cannot call SwigStorage".)

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.

For instructions on how to use SG++ within MATLAB, please see Installation and Usage. However, for this example to work, some extra steps are necessary. In the following, we assume that we want to run the example on Linux.

Please note that in order to get sgpp::optimization to work with MATLAB, you have to disable support for Armadillo and UMFPACK when compiling SG++, i.e. set USE_ARMADILLO and USE_UMFPACK to "no". This is due to incompatible BLAS and LAPACK libraries of Armadillo/UMFPACK and MATLAB (MATLAB uses instead MKL versions of LAPACK and BLAS with different pointer sizes of 64 bits). You can somehow override MATLAB's choice of libraries with the environmental variables BLAS_VERSION and LAPACK_VERSION, but this is strongly discouraged as MATLAB itself may produce unexpected wrong results (e.g., det [1 2; 3 4] = 2). Static linking to Armadillo and UMFPACK would be a possible solution to circumvent this problem.

Now, you should be able to run the MATLAB example, which we describe in the rest of this page.

At the beginning of the program, we disable OpenMP within matsgpp since it interferes with SWIG's director feature.

fprintf('sgpp::optimization example program started.\n\n');
% increase output verbosity
printer = sgpp.OptPrinter.getInstance();
printer.setVerbosity(2);
printLine = @() fprintf([repmat('-', 1, 80) '\n']);

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

% objective function
f = sgpp.OptHimmelblauObjective();
% dimension of domain
d = f.getNumberOfParameters();
% B-spline degree
p = 3;
% maximal number of grid points
N = 30;
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.

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

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

printLine();
fprintf('Generating grid...\n\n');
if ~gridGen.generate()
error('Grid generation failed, exiting.');
end

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

printLine();
fprintf('Hierarchizing...\n\n');
functionValues = gridGen.getFunctionValues();
coeffs = sgpp.DataVector(functionValues.getSize());
hierSLE = sgpp.OptHierarchisationSLE(grid);
sleSolver = sgpp.OptAutoSLESolver();
% solve linear system
if ~sleSolver.solve(hierSLE, functionValues, coeffs)
error('Solving failed, exiting.');
end

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.

printLine();
fprintf('Optimizing smooth interpolant...\n\n');
ft = sgpp.OptInterpolantScalarFunction(grid, coeffs);
x0 = sgpp.DataVector(d);

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.

gridStorage = grid.getStorage();
% index of grid point with minimal function value
x0Index = 0;
fX0 = functionValues.get(0);
for i = 1:functionValues.getSize()-1
if functionValues.get(i) < fX0
fX0 = functionValues.get(i);
x0Index = i;
end
end
x0 = gridStorage.getCoordinates(gridStorage.getPoint(x0Index));
ftX0 = ft.eval(x0);
fprintf(['x0 = ' char(x0.toString()) '\n']);
fprintf(['f(x0) = ' num2str(fX0, 6) ', ft(x0) = ' num2str(ftX0, 6) '\n\n']);

We apply the gradient method and print the results.

fXOpt = f.eval(xOpt);
fprintf(['\nxOpt = ' char(xOpt.toString()) '\n']);
fprintf(['f(xOpt) = ' num2str(fXOpt, 6) ...
', ft(xOpt) = ' num2str(ftXOpt, 6) '\n\n']);

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

printLine();
fprintf('Optimizing objective function (for comparison)...\n\n');
ftXOptNM = ft.eval(xOptNM);
fprintf(['\nxOptNM = ' char(xOptNM.toString()) '\n']);
fprintf(['f(xOptNM) = ' num2str(fXOptNM, 6) ...
', ft(xOptNM) = ' num2str(ftXOptNM, 6) '\n\n']);
printLine();
fprintf('\nsgpp::optimization example program terminated.\n');

The example program outputs the following results:

sgpp::optimization example program started.

--------------------------------------------------------------------------------
Generating grid...

100.0% (N = 29, k = 3)
Done in 0ms.
--------------------------------------------------------------------------------
Hierarchizing...

Solving linear system (automatic method)...
Solving linear system (Gaussian elimination)...
Done in 0ms.
Done in 0ms.
--------------------------------------------------------------------------------
Optimizing smooth interpolant...

x0 = [7.50000000000000000000e-01, 7.50000000000000000000e-01]
f(x0) = 8.125, ft(x0) = 8.125

108 evaluations, x = [8.05312073972314856896e-01, 6.98251390596593024540e-01], f(x) = -2.784842
Done in 0ms.

xOpt = [8.05312073972314856896e-01, 6.98251390596593024540e-01]
f(xOpt) = 0.0927254, ft(xOpt) = -2.78484

--------------------------------------------------------------------------------
Optimizing objective function (for comparison)...


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 one of the four global minima ( $$(0.8, 0.7)$$).