1. 安装依赖

sudo apt-get install cmake libeigen3-dev libatlas-base-dev liblapack-dev libgflags-dev libgoogle-glog-dev libgtest-dev libsuitesparse-dev

依赖主要有：

cmake
Eigen3
BLAS & LAPACK
google-glog + gflags
SuiteSparse and CXSparse (optional)
如果想要安装CXSparse，可以使用sudo apt-get install libcxsparse3.1.4，末尾的版本号可能不一样，可用自动补全来填写

2. 下载源码，编译安装

从github下载源码

git clone https://github.com/ceres-solver/ceres-solver

或者尝试下面的地址下载：

wget http://ceres-solver.org/ceres-solver-1.14.0.tar.gz

如果能翻墙，也可尝试下面的地址

git clone https://ceres-solver.googlesource.com/ceres-solver

编译安装：

## 解压。如果是github上git clone的，就已经是目录了
tar zxf ceres-solver-1.14.0.tar.gz

## 建立编译目录，并进入
cd ceres-solver-1.14.0
mkdir build
cd build

## cmake
cmake ..

## 编译
make -j3

## 测试，可跳过
make test

## 安装
sudo make install

3. 调用示例

建立CMakeLists.txt文件如下：

cmake_minimum_required(VERSION 2.8)

project(hello_world)

find_package(Ceres REQUIRED)
include_directories(${CERES_INCLUDE_DIRS})

add_executable(hello_world main.cpp)
target_link_libraries(hello_world ${CERES_LIBRARIES})

建立main.cpp文件如下：

// A simple example of using the Ceres minimizer.
//
// Minimize 0.5 (10 - x)^2 using jacobian matrix computed using
// automatic differentiation.

#include "ceres/ceres.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] = T(10.0) - x[0];
    return true;
  }
};

int main(int argc, char** argv) {

  // 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;
}

编译运行结果如下：

iter      cost      cost_change  |gradient|   |step|    tr_ratio  tr_radius  ls_iter  iter_time  total_time
   0  4.512500e+01    0.00e+00    9.50e+00   0.00e+00   0.00e+00  1.00e+04        0    5.48e-05    1.16e-03
   1  4.511598e-07    4.51e+01    9.50e-04   9.50e+00   1.00e+00  3.00e+04        1    4.56e-04    2.07e-03
   2  5.012552e-16    4.51e-07    3.17e-08   9.50e-04   1.00e+00  9.00e+04        1    2.82e-05    2.12e-03
Ceres Solver Report: Iterations: 3, Initial cost: 4.512500e+01, Final cost: 5.012552e-16, Termination: CONVERGENCE
x : 0.5 -> 10