Building & Installation

Getting the source code

You can start with the latest stable release . Or if you want the latest version, you can clone the git repository

git clone


Ceres relies on a number of open source libraries, some of which are optional. For details on customizing the build process, see Customizing the build .

  • Eigen 3.2.2 or later strongly recommended, 3.1.0 or later required.


    Ceres can also use Eigen as a sparse linear algebra library. Please see the documentation for EIGENSPARSE for more details.

  • CMake 2.8.0 or later. Required on all platforms except for Android.

  • Google Log 0.3.1 or later. Recommended


    Ceres has a minimal replacement of glog called miniglog that can be enabled with the MINIGLOG build option. miniglog is needed on Android as glog currently does not build using the NDK. It can however be used on other platforms too.

    We do not advise using miniglog on platforms other than Android due to the various performance and functionality compromises in miniglog.


    If you are compiling glog from source, please note that currently, the unit tests for glog (which are enabled by default) do not compile against a default build of gflags 2.1 as the gflags namespace changed from google:: to gflags::. A patch to fix this is available from here.

  • Google Flags. Needed to build examples and tests.

  • SuiteSparse. Needed for solving large sparse linear systems. Optional; strongly recomended for large scale bundle adjustment

  • CXSparse. Similar to SuiteSparse but simpler and slower. CXSparse has no dependencies on LAPACK and BLAS. This makes for a simpler build process and a smaller binary. Optional

  • BLAS and LAPACK routines are needed by SuiteSparse, and optionally used by Ceres directly for some operations.

    On UNIX OSes other than Mac OS X we recommend ATLAS, which includes BLAS and LAPACK routines. It is also possible to use OpenBLAS . However, one needs to be careful to turn off the threading inside OpenBLAS as it conflicts with use of threads in Ceres.

    MAC OS X ships with an optimized LAPACK and BLAS implementation as part of the Accelerate framework. The Ceres build system will automatically detect and use it.

    For Windows things are much more complicated. LAPACK For Windows has detailed instructions..

    Optional but required for SuiteSparse.


We will use Ubuntu as our example linux distribution.


Up to at least Ubuntu 14.04, the SuiteSparse package in the official package repository (built from SuiteSparse v3.4.0) cannot be used to build Ceres as a shared library. Thus if you want to build Ceres as a shared library using SuiteSparse, you must perform a source install of SuiteSparse or use an external PPA (see It is recommended that you use the current version of SuiteSparse (4.2.1 at the time of writing).

Start by installing all the dependencies.

# CMake
sudo apt-get install cmake
# google-glog + gflags
sudo apt-get install libgoogle-glog-dev
sudo apt-get install libatlas-base-dev
# Eigen3
sudo apt-get install libeigen3-dev
# SuiteSparse and CXSparse (optional)
# - If you want to build Ceres as a *static* library (the default)
#   you can use the SuiteSparse package in the main Ubuntu package
#   repository:
sudo apt-get install libsuitesparse-dev
# - However, if you want to build Ceres as a *shared* library, you must
#   add the following PPA:
sudo add-apt-repository ppa:bzindovic/suitesparse-bugfix-1319687
sudo apt-get update
sudo apt-get install libsuitesparse-dev

We are now ready to build, test, and install Ceres.

tar zxf ceres-solver-1.10.0.tar.gz
mkdir ceres-bin
cd ceres-bin
cmake ../ceres-solver-1.10.0
make -j3
make test
make install

You can also try running the command line bundling application with one of the included problems, which comes from the University of Washington’s BAL dataset [Agarwal].

bin/simple_bundle_adjuster ../ceres-solver-1.10.0/data/problem-16-22106-pre.txt

This runs Ceres for a maximum of 10 iterations using the DENSE_SCHUR linear solver. The output should look something like this.

iter      cost      cost_change  |gradient|   |step|    tr_ratio  tr_radius  ls_iter  iter_time  total_time
   0  4.185660e+06    0.00e+00    1.09e+08   0.00e+00   0.00e+00  1.00e+04       0    7.59e-02    3.37e-01
   1  1.062590e+05    4.08e+06    8.99e+06   5.36e+02   9.82e-01  3.00e+04       1    1.65e-01    5.03e-01
   2  4.992817e+04    5.63e+04    8.32e+06   3.19e+02   6.52e-01  3.09e+04       1    1.45e-01    6.48e-01
   3  1.899774e+04    3.09e+04    1.60e+06   1.24e+02   9.77e-01  9.26e+04       1    1.43e-01    7.92e-01
   4  1.808729e+04    9.10e+02    3.97e+05   6.39e+01   9.51e-01  2.78e+05       1    1.45e-01    9.36e-01
   5  1.803399e+04    5.33e+01    1.48e+04   1.23e+01   9.99e-01  8.33e+05       1    1.45e-01    1.08e+00
   6  1.803390e+04    9.02e-02    6.35e+01   8.00e-01   1.00e+00  2.50e+06       1    1.50e-01    1.23e+00

Ceres Solver v1.10.0 Solve Report
                                     Original                  Reduced
Parameter blocks                        22122                    22122
Parameters                              66462                    66462
Residual blocks                         83718                    83718
Residual                               167436                   167436

Minimizer                        TRUST_REGION

Dense linear algebra library            EIGEN
Trust region strategy     LEVENBERG_MARQUARDT

                                        Given                     Used
Linear solver                     DENSE_SCHUR              DENSE_SCHUR
Threads                                     1                        1
Linear solver threads                       1                        1
Linear solver ordering              AUTOMATIC                22106, 16

Initial                          4.185660e+06
Final                            1.803390e+04
Change                           4.167626e+06

Minimizer iterations                        6
Successful steps                            6
Unsuccessful steps                          0

Time (in seconds):
Preprocessor                            0.261

  Residual evaluation                   0.082
  Jacobian evaluation                   0.412
  Linear solver                         0.442
Minimizer                               1.051

Postprocessor                           0.002
Total                                   1.357

Termination:                      CONVERGENCE (Function tolerance reached. |cost_change|/cost: 1.769766e-09 <= 1.000000e-06)

Mac OS X


Ceres will not compile using Xcode 4.5.x (Clang version 4.1) due to a bug in that version of Clang. If you are running Xcode 4.5.x, please update to Xcode >= 4.6.x before attempting to build Ceres.

On OS X, you can either use MacPorts or homebrew to install Ceres Solver.

If using MacPorts, then

sudo port install ceres-solver

will install the latest version.

If using homebrew and assuming that you have the homebrew/science [1] tap enabled, then

brew install ceres-solver

will install the latest stable version along with all the required dependencies and

brew install ceres-solver --HEAD

will install the latest version in the git repo.

You can also install each of the dependencies by hand using homebrew. There is no need to install BLAS or LAPACK separately as OS X ships with optimized BLAS and LAPACK routines as part of the vecLib framework.

# CMake
brew install cmake
# google-glog and gflags
brew install glog
# Eigen3
brew install eigen
# SuiteSparse and CXSparse
brew install suite-sparse

We are now ready to build, test, and install Ceres.

tar zxf ceres-solver-1.10.0.tar.gz
mkdir ceres-bin
cd ceres-bin
cmake ../ceres-solver-1.10.0
make -j3
make test
make install

Like the Linux build, you should now be able to run bin/simple_bundle_adjuster.



Ceres and many of its dependencies are in homebrew/science tap. So, if you don’t have this tap enabled, then you will need to enable it as follows before executing any of the commands in this section.

brew tap homebrew/science



If you find the following CMake difficult to set up, then you may be interested in a Microsoft Visual Studio wrapper for Ceres Solver by Tal Ben-Nun.

On Windows, we support building with Visual Studio 2010 or newer. Note that the Windows port is less featureful and less tested than the Linux or Mac OS X versions due to the lack of an officially supported way of building SuiteSparse and CXSparse. There are however a number of unofficial ways of building these libraries. Building on Windows also a bit more involved since there is no automated way to install dependencies.


Using google-glog & miniglog with windows.h.

The windows.h header if used with GDI (Graphics Device Interface) defines ERROR, which conflicts with the definition of ERROR as a LogSeverity level in google-glog and miniglog. There are at least two possible fixes to this problem:

  1. Use google-glog and define GLOG_NO_ABBREVIATED_SEVERITIES when building Ceres and your own project, as documented here. Note that this fix will not work for miniglog, but use of miniglog is strongly discouraged on any platform for which google-glog is available (which includes Windows).
  2. If you do not require GDI, then define NOGDI before including windows.h. This solution should work for both google-glog and miniglog and is documented for google-glog here.
  1. Make a toplevel directory for deps & build & src somewhere: ceres/

  2. Get dependencies; unpack them as subdirectories in ceres/ (ceres/eigen, ceres/glog, etc)

    1. Eigen 3.1 (needed on Windows; 3.0.x will not work). There is no need to build anything; just unpack the source tarball.
    2. google-glog Open up the Visual Studio solution and build it.
    3. gflags Open up the Visual Studio solution and build it.
    4. (Experimental) SuiteSparse Previously SuiteSparse was not available on Windows, recently it has become possible to build it on Windows using the suitesparse-metis-for-windows project. If you wish to use SuiteSparse, follow their instructions for obtaining and building it.
    5. (Experimental) CXSparse Previously CXSparse was not available on Windows, there are now several ports that enable it to be, including: [1] and [2]. If you wish to use CXSparse, follow their instructions for obtaining and building it.
  3. Unpack the Ceres tarball into ceres. For the tarball, you should get a directory inside ceres similar to ceres-solver-1.3.0. Alternately, checkout Ceres via git to get ceres-solver.git inside ceres.

  4. Install CMake,

  5. Make a dir ceres/ceres-bin (for an out-of-tree build)

  6. Run CMake; select the ceres-solver-X.Y.Z or ceres-solver.git directory for the CMake file. Then select the ceres-bin for the build dir.

  7. Try running Configure. It won’t work. It’ll show a bunch of options. You’ll need to set:


    to the appropriate directories where you unpacked/built them. If any of the variables are not visible in the CMake GUI, create a new entry for them. We recommend using the <NAME>_(INCLUDE/LIBRARY)_DIR_HINTS variables rather than setting the <NAME>_INCLUDE_DIR & <NAME>_LIBRARY variables directly to keep all of the validity checking, and to avoid having to specify the library files manually.

  8. You may have to tweak some more settings to generate a MSVC project. After each adjustment, try pressing Configure & Generate until it generates successfully.

  9. Open the solution and build it in MSVC

To run the tests, select the RUN_TESTS target and hit Build RUN_TESTS from the build menu.

Like the Linux build, you should now be able to run bin/simple_bundle_adjuster.


  1. The default build is Debug; consider switching it to release mode.
  2. Currently system_test is not working properly.
  3. CMake puts the resulting test binaries in ceres-bin/examples/Debug by default.
  4. The solvers supported on Windows are DENSE_QR, DENSE_SCHUR, CGNR, and ITERATIVE_SCHUR.
  5. We’re looking for someone to work with upstream SuiteSparse to port their build system to something sane like CMake, and get a fully supported Windows port.


Download the Android NDK version r9d or later. Run ndk-build from inside the jni directory. Use the libceres.a that gets created.



You need iOS version 6.0 or higher to build Ceres Solver.

To build Ceres for iOS, we need to force CMake to find the toolchains from the iOS SDK instead of using the standard ones. For example:

cmake ../ceres-solver \
-DCMAKE_TOOLCHAIN_FILE=../ceres-solver/cmake/iOS.cmake \
-DEIGEN_INCLUDE_DIR=/path/to/eigen/header \

PLATFORM can be one of OS, SIMULATOR and SIMULATOR64. You can build for OS (armv7, armv7s, arm64), SIMULATOR (i386) or SIMULATOR64 (x86_64) separately and use LIPO to merge them into one static library. See cmake/iOS.cmake for more options.

After building, you will get a libceres.a library, which you will need to add to your Xcode project.

The default CMake configuration builds a bare bones version of Ceres Solver that only depends on Eigen (MINIGLOG is compiled into Ceres if it is used), this should be sufficient for solving small to moderate sized problems (No SPARSE_SCHUR, SPARSE_NORMAL_CHOLESKY linear solvers and no CLUSTER_JACOBI and CLUSTER_TRIDIAGONAL preconditioners).

If you decide to use LAPACK and BLAS, then you also need to add Accelerate.framework to your XCode project’s linking dependency.

Customizing the build

It is possible to reduce the libraries needed to build Ceres and customize the build process by setting the appropriate options in CMake. These options can either be set in the CMake GUI, or via -D<OPTION>=<ON/OFF> when running CMake from the command line. In general, you should only modify these options from their defaults if you know what you are doing.


If you are setting variables via -D<VARIABLE>=<VALUE> when calling CMake, it is important to understand that this forcibly overwrites the variable <VARIABLE> in the CMake cache at the start of every configure.

This can lead to confusion if you are invoking the CMake curses terminal GUI (via ccmake, e.g. `ccmake -D<VARIABLE>=<VALUE> <PATH_TO_SRC>). In this case, even if you change the value of <VARIABLE> in the CMake GUI, your changes will be overwritten with the value passed via -D<VARIABLE>=<VALUE> (if one exists) at the start of each configure.

As such, it is generally easier not to pass values to CMake via -D and instead interactively experiment with their values in the CMake GUI. If they are not present in the Standard View, toggle to the Advanced View with <t>.

Options controlling Ceres configuration

  1. LAPACK [Default: ON]: By default Ceres will use LAPACK (& BLAS) if they are found. Turn this OFF to build Ceres without LAPACK. Turning this OFF also disables SUITESPARSE as it depends on LAPACK.

  2. SUITESPARSE [Default: ON]: By default, Ceres will link to SuiteSparse if it and all of its dependencies are present. Turn this OFF to build Ceres without SuiteSparse. Note that LAPACK must be ON in order to build with SuiteSparse.

  3. CXSPARSE [Default: ON]: By default, Ceres will link to CXSparse if all its dependencies are present. Turn this OFF to build Ceres without CXSparse.

  4. EIGENSPARSE [Default: OFF]: By default, Ceres will not use Eigen’s sparse Cholesky factorization. The is because this part of the code is licensed under the LGPL and since Eigen is a header only library, including this code will result in an LGPL licensed version of Ceres.


    For good performance, use Eigen version 3.2.2 or later.

  5. GFLAGS [Default: ON]: Turn this OFF to build Ceres without gflags. This will also prevent some of the example code from building.

  6. MINIGLOG [Default: OFF]: Ceres includes a stripped-down, minimal implementation of glog which can optionally be used as a substitute for glog, thus removing glog as a required dependency. Turn this ON to use this minimal glog implementation.

  7. SCHUR_SPECIALIZATIONS [Default: ON]: If you are concerned about binary size/compilation time over some small (10-20%) performance gains in the SPARSE_SCHUR solver, you can disable some of the template specializations by turning this OFF.

  8. OPENMP [Default: ON]: On certain platforms like Android, multi-threading with OpenMP is not supported. Turn this OFF to disable multi-threading.

  9. BUILD_SHARED_LIBS [Default: OFF]: By default Ceres is built as a static library, turn this ON to instead build Ceres as a shared library.

  10. BUILD_DOCUMENTATION [Default: OFF]: Use this to enable building the documentation, requires Sphinx and the sphinx_rtd_theme package available from the Python package index. In addition, make ceres_docs can be used to build only the documentation.

  11. MSVC_USE_STATIC_CRT [Default: OFF] Windows Only: By default Ceres will use the Visual Studio default, shared C-Run Time (CRT) library. Turn this ON to use the static C-Run Time library instead.

Options controlling Ceres dependency locations

Ceres uses the CMake find_package function to find all of its dependencies using Find<DEPENDENCY_NAME>.cmake scripts which are either included in Ceres (for most dependencies) or are shipped as standard with CMake (for LAPACK & BLAS). These scripts will search all of the “standard” install locations for various OSs for each dependency. However, particularly for Windows, they may fail to find the library, in this case you will have to manually specify its installed location. The Find<DEPENDENCY_NAME>.cmake scripts shipped with Ceres support two ways for you to do this:

  1. Set the hints variables specifying the directories to search in preference, but in addition, to the search directories in the Find<DEPENDENCY_NAME>.cmake script:


    These variables should be set via -D<VAR>=<VALUE> CMake arguments as they are not visible in the GUI.

  2. Set the variables specifying the explicit include directory and library file to use:


    This bypasses all searching in the Find<DEPENDENCY_NAME>.cmake script, but validation is still performed.

    These variables are available to set in the CMake GUI. They are visible in the Standard View if the library has not been found (but the current Ceres configuration requires it), but are always visible in the Advanced View. They can also be set directly via -D<VAR>=<VALUE> arguments to CMake.

Building using custom BLAS & LAPACK installs

If the standard find package scripts for BLAS & LAPACK which ship with CMake fail to find the desired libraries on your system, try setting CMAKE_LIBRARY_PATH to the path(s) to the directories containing the BLAS & LAPACK libraries when invoking CMake to build Ceres via -D<VAR>=<VALUE>. This should result in the libraries being found for any common variant of each.

If you are building on an exotic system, or setting CMAKE_LIBRARY_PATH does not work, or is not appropriate for some other reason, one option would be to write your own custom versions of FindBLAS.cmake & FindLAPACK.cmake specific to your environment. In this case you must set CMAKE_MODULE_PATH to the directory containing these custom scripts when invoking CMake to build Ceres and they will be used in preference to the default versions. However, in order for this to work, your scripts must provide the full set of variables provided by the default scripts. Also, if you are building Ceres with SuiteSparse, the versions of BLAS & LAPACK used by SuiteSparse and Ceres should be the same.

Using Ceres with CMake

Once the library is installed with make install, it is possible to use CMake with find_package() in order to compile user code against Ceres. For example, to compile examples/ in a separate, standalone project, the following CMakeList.txt can be used:

cmake_minimum_required(VERSION 2.8)


find_package(Ceres REQUIRED)

# helloworld
target_link_libraries(helloworld ${CERES_LIBRARIES})

Understanding the CMake Package System

Although a full tutorial on CMake is outside the scope of this guide, here we cover some of the most common CMake misunderstandings that crop up when using Ceres. For more detailed CMake usage, the following references are very useful:

  • The official CMake tutorial

    Provides a tour of the core features of CMake.

  • ProjectConfig tutorial and the cmake-packages documentation

    Cover how to write a ProjectConfig.cmake file, discussed below, for your own project when installing or exporting it using CMake. It also covers how these processes in conjunction with find_package() are actually handled by CMake. The ProjectConfig tutorial is the older style, currently used by Ceres for compatibility with older versions of CMake.


    Targets in CMake.

    All libraries and executables built using CMake are represented as targets created using add_library() and add_executable(). Targets encapsulate the rules and dependencies (which can be other targets) required to build or link against an object. This allows CMake to implicitly manage dependency chains. Thus it is sufficient to tell CMake that a library target: B depends on a previously declared library target A, and CMake will understand that this means that B also depends on all of the public dependencies of A.

When a project like Ceres is installed using CMake, in addition to the public headers and compiled libraries, a set of CMake-specific project configuration files are also installed to: <INSTALL_ROOT>/share/Ceres. When find_package is invoked, CMake checks various standard install locations (including /usr/local on Linux & UNIX systems), for installed CMake configuration files for the project to be found (i.e. Ceres in the case of find_package(Ceres)). Specifically it looks for:

  • <PROJECT_NAME>Config.cmake (or <lower_case_project_name>-config.cmake)

    Which is written by the developers of the project, and is configured with the selected options and installed locations when the project is built and defines the CMake variables: <PROJECT_NAME>_INCLUDE_DIRS & <PROJECT_NAME>_LIBRARIES which are used by the caller to import the project.

The <PROJECT_NAME>Config.cmake typically includes a second file installed to the same location:

  • <PROJECT_NAME>Targets.cmake

    Which is autogenerated by CMake as part of the install process and defines imported targets for the project in the caller’s CMake scope.

An imported target contains the same information about a library as a CMake target that was declared locally in the current CMake project using add_library(). However, imported targets refer to objects that have already been built previously by a different CMake project. Principally, an imported target contains the location of the compiled object and all of its public dependencies required to link against it. Any locally declared target can depend on an imported target, and CMake will manage the dependency chain, just as if the imported target had been declared locally by the current project.

Crucially, just like any locally declared CMake target, an imported target is identified by its name when adding it as a dependency to another target.

Thus, if in a project using Ceres you had the following in your CMakeLists.txt:

find_package(Ceres REQUIRED)

You would see the output: CERES_LIBRARIES = ceres. However, here ceres is an imported target created when CeresTargets.cmake was read as part of find_package(Ceres REQUIRED). It does not refer (directly) to the compiled Ceres library: libceres.a/so/dylib/lib. This distinction is important, as depending on the options selected when it was built, Ceres can have public link dependencies which are encapsulated in the imported target and automatically added to the link step when Ceres is added as a dependency of another target by CMake. In this case, linking only against libceres.a/so/dylib/lib without these other public dependencies would result in a linker error.

Although this description covers projects that are installed using CMake, it also holds for projects that are exported using CMake using export() instead of install(). When a project is installed, the compiled libraries and headers are copied from the source & build directory to the install location, and it is these copied files that are used by any client code. When a project is exported, instead of copying the compiled libraries and headers, CMake creates an entry for the project in <USER_HOME>/.cmake/packages which contains the path to the project’s build directory which will be checked by CMake during a call to find_package(). The effect of which is that any client code uses the compiled libraries and headers in the build directory directly, thus not requiring the project to be installed to be used.

Installing / Exporting a project that uses Ceres

As described in Understanding the CMake Package System, the contents of the CERES_LIBRARIES variable is the name of an imported target which represents Ceres. If you are installing / exporting your own project which uses Ceres, it is important to understand that:

imported targets are not (re)exported when a project which imported them is exported.

Thus, when a project Foo which uses Ceres is exported, its list of dependencies as seen by another project Bar which imports Foo via: find_package(Foo REQUIRED) will contain: ceres. However, the definition of ceres as an imported target is not (re)exported when Foo is exported. Hence, without any additional steps, when processing Bar, ceres will not be defined as an imported target. Thus, when processing Bar, CMake will assume that ceres refers only to: libceres.a/so/dylib/lib (the compiled Ceres library) directly if it is on the current list of search paths. In which case, no CMake errors will occur, but Bar will not link properly, as it does not have the required public link dependencies of Ceres, which are stored in the imported target defintion.

The solution to this is for Foo (i.e., the project that uses Ceres) to invoke find_package(Ceres) in FooConfig.cmake, thus ceres will be defined as an imported target when CMake processes Bar. An example of the required modifications to FooConfig.cmake are show below:

# Importing Ceres in FooConfig.cmake using CMake 2.8.x style.
# When configure_file() is used to generate FooConfig.cmake from
#, @Ceres_DIR@ will be replaced with the current
# value of Ceres_DIR being used by Foo.  This should be passed as a hint
# when invoking find_package(Ceres) to ensure that the same install of
# Ceres is used as was used to build Foo.

# Forward the QUIET / REQUIRED options.
   find_package(Ceres QUIET HINTS ${CERES_DIR_HINTS})
elseif (Foo_FIND_REQUIRED)
   find_package(Ceres REQUIRED HINTS ${CERES_DIR_HINTS})
else ()
   find_package(Ceres HINTS ${CERES_DIR_HINTS})
# Importing Ceres in FooConfig.cmake using CMake 3.x style.
# In CMake v3.x, the find_dependency() macro exists to forward the REQUIRED
# / QUIET parameters to find_package() when searching for dependencies.
# Note that find_dependency() does not take a path hint, so if Ceres was
# installed in a non-standard location, that location must be added to
# CMake's search list before this call.

Specify Ceres version

Additionally, when CMake has found Ceres it can check the package version, if it has been specified in the find_package() call. For example:

find_package(Ceres 1.2.3 REQUIRED)

The version is an optional argument.

Local installations

If Ceres was installed in a non-standard path by specifying -DCMAKE_INSTALL_PREFIX="/some/where/local", then the user should add the PATHS option to the find_package() command, e.g.,

find_package(Ceres REQUIRED PATHS "/some/where/local/")

Note that this can be used to have multiple versions of Ceres installed.