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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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//
// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// A simple example of optimizing a sampled function by using cubic
// interpolation.
#include "ceres/ceres.h"
#include "ceres/cubic_interpolation.h"
#include "glog/logging.h"
using Interpolator = ceres::CubicInterpolator<ceres::Grid1D<double>>;
// A simple cost functor that interfaces an interpolated table of
// values with automatic differentiation.
struct InterpolatedCostFunctor {
explicit InterpolatedCostFunctor(const Interpolator& interpolator)
: interpolator(interpolator) {}
template <typename T>
bool operator()(const T* x, T* residuals) const {
interpolator.Evaluate(*x, residuals);
return true;
}
static ceres::CostFunction* Create(const Interpolator& interpolator) {
return new ceres::AutoDiffCostFunction<InterpolatedCostFunctor, 1, 1>(
interpolator);
}
private:
const Interpolator& interpolator;
};
int main(int argc, char** argv) {
google::InitGoogleLogging(argv[0]);
// Evaluate the function f(x) = (x - 4.5)^2;
const int kNumSamples = 10;
double values[kNumSamples];
for (int i = 0; i < kNumSamples; ++i) {
values[i] = (i - 4.5) * (i - 4.5);
}
ceres::Grid1D<double> array(values, 0, kNumSamples);
Interpolator interpolator(array);
double x = 1.0;
ceres::Problem problem;
ceres::CostFunction* cost_function =
InterpolatedCostFunctor::Create(interpolator);
problem.AddResidualBlock(cost_function, nullptr, &x);
ceres::Solver::Options options;
options.minimizer_progress_to_stdout = true;
ceres::Solver::Summary summary;
ceres::Solve(options, &problem, &summary);
std::cout << summary.BriefReport() << "\n";
std::cout << "Expected x: 4.5. Actual x : " << x << std::endl;
return 0;
}