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+====
+Why?
+====
+.. _chapter-features:
+
+* **Code Quality** - Ceres Solver has been used in production at
+  Google for more than four years now. It is clean, extensively tested
+  and well documented code that is actively developed and supported.
+
+* **Modeling API** - It is rarely the case that one starts with the
+  exact and complete formulation of the problem that one is trying to
+  solve. Ceres's modeling API has been designed so that the user can
+  easily build and modify the objective function, one term at a
+  time. And to do so without worrying about how the solver is going to
+  deal with the resulting changes in the sparsity/structure of the
+  underlying problem.
+
+  - **Derivatives** Supplying derivatives is perhaps the most tedious
+    and error prone part of using an optimization library.  Ceres
+    ships with `automatic`_ and `numeric`_ differentiation. So you
+    never have to compute derivatives by hand (unless you really want
+    to). Not only this, Ceres allows you to mix automatic, numeric and
+    analytical derivatives in any combination that you want.
+
+  - **Robust Loss Functions** Most non-linear least squares problems
+    involve data. If there is data, there will be outliers. Ceres
+    allows the user to *shape* their residuals using a
+    :class:`LossFunction` to reduce the influence of outliers.
+
+  - **Local Parameterization** In many cases, some parameters lie on a
+    manifold other than Euclidean space, e.g., rotation matrices. In
+    such cases, the user can specify the geometry of the local tangent
+    space by specifying a :class:`LocalParameterization` object.
+
+* **Solver Choice** Depending on the size, sparsity structure, time &
+  memory budgets, and solution quality requirements, different
+  optimization algorithms will suit different needs. To this end,
+  Ceres Solver comes with a variety of optimization algorithms:
+
+  - **Trust Region Solvers** - Ceres supports Levenberg-Marquardt,
+    Powell's Dogleg, and Subspace dogleg methods. The key
+    computational cost in all of these methods is the solution of a
+    linear system. To this end Ceres ships with a variety of linear
+    solvers - dense QR and dense Cholesky factorization (using
+    `Eigen`_ or `LAPACK`_) for dense problems, sparse Cholesky
+    factorization (`SuiteSparse`_, `CXSparse`_ or `Eigen`_) for large
+    sparse problems custom Schur complement based dense, sparse, and
+    iterative linear solvers for `bundle adjustment`_ problems.
+
+  - **Line Search Solvers** - When the problem size is so large that
+    storing and factoring the Jacobian is not feasible or a low
+    accuracy solution is required cheaply, Ceres offers a number of
+    line search based algorithms. This includes a number of variants
+    of Non-linear Conjugate Gradients, BFGS and LBFGS.
+
+* **Speed** - Ceres Solver has been extensively optimized, with C++
+  templating, hand written linear algebra routines and OpenMP or C++11 threads
+  based multithreading of the Jacobian evaluation and the linear solvers.
+
+* **Solution Quality** Ceres is the `best performing`_ solver on the NIST
+  problem set used by Mondragon and Borchers for benchmarking
+  non-linear least squares solvers.
+
+* **Covariance estimation** - Evaluate the sensitivity/uncertainty of
+  the solution by evaluating all or part of the covariance
+  matrix. Ceres is one of the few solvers that allows you to to do
+  this analysis at scale.
+
+* **Community** Since its release as an open source software, Ceres
+  has developed an active developer community that contributes new
+  features, bug fixes and support.
+
+* **Portability** - Runs on *Linux*, *Windows*, *Mac OS X*, *Android*
+  *and iOS*.
+
+* **BSD Licensed** The BSD license offers the flexibility to ship your
+  application
+
+.. _best performing: https://groups.google.com/forum/#!topic/ceres-solver/UcicgMPgbXw
+.. _bundle adjustment: http://en.wikipedia.org/wiki/Bundle_adjustment
+.. _SuiteSparse: http://www.cise.ufl.edu/research/sparse/SuiteSparse/
+.. _Eigen: http://eigen.tuxfamily.org/
+.. _LAPACK: http://www.netlib.org/lapack/
+.. _CXSparse: https://www.cise.ufl.edu/research/sparse/CXSparse/
+.. _automatic: http://en.wikipedia.org/wiki/Automatic_differentiation
+.. _numeric: http://en.wikipedia.org/wiki/Numerical_differentiation