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Using a user defined gradient function
beniz edited this page Dec 19, 2014
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Sometimes, the objective function's symbolic gradient, or some approximation of it, is known. In this case, black box optimization methods such as CMA-ES may prove costly compared to others.
However, in these cases, libcmaes allows to use the gradient as extra information to speed-up convergence.
Below is a basic example that can be found in examples/sample-code-gradient.cc :
#include "cmaes.h"
#include <iostream>
using namespace libcmaes;
FitFunc fsphere = [](const double *x, const int N)
{
double val = 0.0;
for (int i=0;i<N;i++)
val += x[i]*x[i];
return val;
};
GradFunc grad_fsphere = [](const double *x, const int N)
{
dVec grad(N);
for (int i=0;i<N;i++)
grad(i) = 2.0*x[i];
return grad;
};
int main(int argc, char *argv[])
{
int dim = 10; // problem dimensions.
std::vector<double> x0(dim,10.0);
double sigma = 0.1;
//int lambda = 100; // offsprings at each generation.
CMAParameters<> cmaparams(x0,sigma);
cmaparams.set_algo(aCMAES);
CMASolutions cmasols = cmaes<>(fsphere,cmaparams,
CMAStrategy<CovarianceUpdate>::_defaultPFunc, // use default progress function
grad_fsphere);
std::cout << "best solution: " << cmasols << std::endl;
std::cout << "optimization took " << cmasols.elapsed_time() / 1000.0 << " seconds\n";
return cmasols.run_status();
}
In summary, injection of the gradient is as easy as the definition of the GradFunc function above, and its passing as parameter to the cmaes<> call.