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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)
//
// Example of minimizing the Rosenbrock function
// (https://en.wikipedia.org/wiki/Rosenbrock_function) using
// GradientProblemSolver using automatically computed derivatives.
#include "absl/log/initialize.h"
#include "ceres/ceres.h"
// f(x,y) = (1-x)^2 + 100(y - x^2)^2;
struct Rosenbrock {
template <typename T>
bool operator()(const T* parameters, T* cost) const {
const T x = parameters[0];
const T y = parameters[1];
cost[0] = (1.0 - x) * (1.0 - x) + 100.0 * (y - x * x) * (y - x * x);
return true;
}
static ceres::FirstOrderFunction* Create() {
constexpr int kNumParameters = 2;
return new ceres::AutoDiffFirstOrderFunction<Rosenbrock, kNumParameters>(
new Rosenbrock);
}
};
int main(int argc, char** argv) {
absl::InitializeLog();
double parameters[2] = {-1.2, 1.0};
ceres::GradientProblemSolver::Options options;
options.minimizer_progress_to_stdout = true;
ceres::GradientProblemSolver::Summary summary;
ceres::GradientProblem problem(Rosenbrock::Create());
ceres::Solve(options, problem, parameters, &summary);
std::cout << summary.FullReport() << "\n";
std::cout << "Initial x: " << -1.2 << " y: " << 1.0 << "\n";
std::cout << "Final x: " << parameters[0] << " y: " << parameters[1]
<< "\n";
return 0;
}