Refer to the official website for tutorials: http://www.ceres-solver.org/nnls_tutorial.html
Hello World!
Consider the following optimization problem:
Step 1: Write a cost function
struct CostFunctor {template <typename T>
bool operator()(const T* const x, T* residual) const {
residual[0] = T(10.0) - x[0];return true;
}
};
operator() is a templated method, yes input and output can have different types, this is introduced in "C++ primer plus".
Step 2: There is already a residual function of the class, and a nonlinear optimization problem can be constructed.
int main(int argc, char** argv) {
google::InitGoogleLogging(argv[0]); //What does this sentence mean, it doesn't matter if you don't feel it, in Gao Xiang's "Fourteen Lectures on Visual SLAM" Found this sentence, it should not affect.
// The variable to solve for with its initial value.
double initial_x = 5.0;
double x = initial_x;
// Build the problem.
Problem problem;
// Set up the only cost function (also known as residual). This uses
// auto-differentiation to obtain the derivative (jacobian).
CostFunction* cost_function =
new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
problem.AddResidualBlock(cost_function, NULL, &x); //cost function, kernel function, to be optimized Variables
// Run the solver!
Solver::Options options;
options.linear_solver_type = ceres::DENSE_QR;//QR分解
options.minimizer_progress_to_stdout = true;//输出到cout
Solver::Summary summary;//优化信息
Solve(options, &problem, &summary);//开始计算
std::cout << summary.BriefReport() << "\n";
std::cout << "x : " << initial_x
<< " -> " << x << "\n";
return 0;
}
The whole code looks like this:
#include "ceres/ceres.h"
#include "glog/logging.h"
using ceres::AutoDiffCostFunction;
using ceres::CostFunction;
using ceres::Problem;
using ceres::Solver;
using ceres::Solve;
// A templated cost functor that implements the residual r = 10 -
// x. The method operator() is templated so that we can then use an
// automatic differentiation wrapper around it to generate its
// derivatives.
struct CostFunctor {
template <typename T> bool operator()(const T* const x, T* residual) const {
residual[0] = 10.0 - x[0];
return true;
}
};
int main(int argc, char** argv) {
google::InitGoogleLogging(argv[0]);
// The variable to solve for with its initial value. It will be
// mutated in place by the solver.
double x = 0.5;
const double initial_x = x;
// Build the problem.
Problem problem;
// Set up the only cost function (also known as residual). This uses
// auto-differentiation to obtain the derivative (jacobian).
CostFunction* cost_function =
new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
problem.AddResidualBlock(cost_function, NULL, &x);
// Run the solver!
Solver::Options options;
options.minimizer_progress_to_stdout = true;
Solver::Summary summary;
Solve(options, &problem, &summary);
std::cout << summary.BriefReport() << "\n";
std::cout << "x : " << initial_x
<< " -> " << x << "\n";
return 0;
}
The CMakeLists.txt file is as follows:
cmake_minimum_required(VERSION 2.8)
project(ceres)
#set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules)
find_package(Ceres REQUIRED)
include_directories(${CERES_INCLUDE_DIRS})
add_executable(use_ceres main.cpp)
target_link_libraries(use_ceres ${CERES_LIBRARIES})
The results are as follows:
