mirror of
https://github.com/ceres-solver/ceres-solver.git
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Sameer Agarwal
7b6bd1cd31
1. Complete restructuring of the documentation to account for GradientProblemSolver. 2. Update the version history to account for changes since 1.9.0. 3. Add links and document the various examples that ship with ceres. 4. Documentation for GradientProblem GradientProblemSolver. Change-Id: If3a18f2850cbc98be1bc34435e9ea468785b8b27
87 lines
4.2 KiB
ReStructuredText
87 lines
4.2 KiB
ReStructuredText
========
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Features
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========
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.. _chapter-features:
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* **Code Quality** - Ceres Solver has been used in production at
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Google for more than four years now. It is clean, extensively tested
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and well documented code that is actively developed and supported.
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* **Modeling API** - It is rarely the case that one starts with the
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exact and complete formulation of the problem that one is trying to
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solve. Ceres's modeling API has been designed so that the user can
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easily build and modify the objective function, one term at a
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time. And to do so without worrying about how the solver is going to
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deal with the resulting changes in the sparsity/structure of the
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underlying problem.
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- **Derivatives** Supplying derivatives is perhaps the most tedious
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and error prone part of using an optimization library. Ceres
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ships with `automatic`_ and `numeric`_ differentiation. So you
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never have to compute derivatives by hand (unless you really want
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to). Not only this, Ceres allows you to mix automatic, numeric and
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analytical derivatives in any combination that you want.
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- **Robust Loss Functions** Most non-linear least squares problems
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involve data. If there is data, there will be outliers. Ceres
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allows the user to *shape* their residuals using a
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:class:`LossFunction` to reduce the influence of outliers.
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- **Local Parameterization** In many cases, some parameters lie on a
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manifold other than Euclidean space, e.g., rotation matrices. In
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such cases, the user can specify the geometry of the local tangent
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space by specifying a :class:`LocalParameterization` object.
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* **Solver Choice** Depending on the size, sparsity structure, time &
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memory budgets, and solution quality requiremnts, different
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optimization algorithms will suit different needs. To this end,
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Ceres Solver comes with a variety of optimization algorithms:
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- **Trust Region Solvers** - Ceres supports Levenberg-Marquardt,
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Powell's Dogleg, and Subspace dogleg methods. The key
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computational cost in all of these methods is the solution of a
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linear system. To this end Ceres ships with a variety of linear
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solvers - dense QR and dense Cholesky factorization (using
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`Eigen`_ or `LAPACK`_) for dense problems, sparse Cholesky
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factorization (`SuiteSparse`_, `CXSparse`_ or `Eigen`_) for large
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sparse problems custom Schur complement based dense, sparse, and
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iterative linear solvers for `bundle adjustment`_ problems.
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- **Line Search Solvers** - When the problem size is so large that
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storing and factoring the Jacobian is not feasible or a low
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accuracy solution is required cheaply, Ceres offers a number of
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line search based algorithms. This includes a number of variants
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of Non-linear Conjugate Gradients, BFGS and LBFGS.
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* **Speed** - Ceres Solver has been extensively optimized, with C++
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templating, hand written linear algebra routines and OpenMP based
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multithreading of the Jacobian evaluation and the linear solvers.
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* **Solution Quality** Ceres is the `best performing`_ solver on the NIST
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problem set used by Mondragon and Borchers for benchmarking
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non-linear least squares solvers.
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* **Covariance estimation** - Evaluate the sensitivity/uncertainty of
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the solution by evaluating all or part of the covariance
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matrix. Ceres is one of the few solvers that allows you to to do
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this analysis at scale.
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* **Community** Since its release as an open source software, Ceres
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has developed an active developer community that contributes new
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features, bug fixes and support.
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* **Portability** - Runs on *Linux*, *Windows*, *Mac OS X*, *Android*
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*and iOS*.
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* **BSD Licensed** The BSD license offers the flexibility to ship your
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application
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.. _best performing: https://groups.google.com/forum/#!topic/ceres-solver/UcicgMPgbXw
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.. _bundle adjustment: http://en.wikipedia.org/wiki/Bundle_adjustment
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.. _SuiteSparse: http://www.cise.ufl.edu/research/sparse/SuiteSparse/
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.. _Eigen: http://eigen.tuxfamily.org/
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.. _LAPACK: http://www.netlib.org/lapack/
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.. _CXSparse: https://www.cise.ufl.edu/research/sparse/CXSparse/
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.. _automatic: http://en.wikipedia.org/wiki/Automatic_differentiation
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.. _numeric: http://en.wikipedia.org/wiki/Numerical_differentiation
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