.. _chapter-solving_faqs:

.. default-domain:: cpp

.. cpp:namespace:: ceres

=======
Solving
=======

How do I evaluate the Jacobian for a solved problem?
====================================================

Using :func:`Problem::Evaluate`.

How do I choose the right linear solver?
========================================

When using the ``TRUST_REGION`` minimizer, the choice of linear solver is an
important decision. It affects solution quality and runtime. Here is a simple
way to reason about it.

1. For small (a few hundred parameters) or dense problems use
   ``DENSE_QR``.

2. For general sparse problems (i.e., the Jacobian matrix has a
   substantial number of zeros) use
   ``SPARSE_NORMAL_CHOLESKY``.

3. For bundle adjustment problems with up to a hundred or so
   cameras, use ``DENSE_SCHUR``.

4. For larger bundle adjustment problems with sparse Schur
   Complement/Reduced camera matrices use ``SPARSE_SCHUR``.

   If you do not have access to these libraries for whatever
   reason, ``ITERATIVE_SCHUR`` with ``SCHUR_JACOBI`` is an
   excellent alternative.

5. For large bundle adjustment problems (a few thousand cameras or
   more) use the ``ITERATIVE_SCHUR`` solver. There are a number of
   preconditioner choices here. ``SCHUR_JACOBI`` offers an
   excellent balance of speed and accuracy. This is also the
   recommended option if you are solving medium sized problems for
   which ``DENSE_SCHUR`` is too slow but ``SuiteSparse`` is not
   available.

   .. NOTE::

     If you are solving small to medium sized problems, consider
     setting ``Solver::Options::use_explicit_schur_complement`` to
     ``true``, it can result in a substantial performance boost.

   If you are not satisfied with ``SCHUR_JACOBI``'s performance try
   ``CLUSTER_JACOBI`` and ``CLUSTER_TRIDIAGONAL`` in that
   order. They require that you have ``SuiteSparse``
   installed. Both of these preconditioners use a clustering
   algorithm. Use ``SINGLE_LINKAGE`` before ``CANONICAL_VIEWS``.

Use :func:`Solver::Summary::FullReport` to diagnose performance problems
=========================================================================

When diagnosing Ceres performance issues - runtime and convergence, the first
place to start is by looking at the output of ``Solver::Summary::FullReport``.
Here is an example

.. code-block:: console

   ./bin/bundle_adjuster --input ../data/problem-16-22106-pre.txt

   iter      cost      cost_change  |gradient|   |step|    tr_ratio  tr_radius  ls_iter  iter_time  total_time
      0  4.185660e+06    0.00e+00    2.16e+07   0.00e+00   0.00e+00  1.00e+04       0    7.50e-02    3.58e-01
      1  1.980525e+05    3.99e+06    5.34e+06   2.40e+03   9.60e-01  3.00e+04       1    1.84e-01    5.42e-01
      2  5.086543e+04    1.47e+05    2.11e+06   1.01e+03   8.22e-01  4.09e+04       1    1.53e-01    6.95e-01
      3  1.859667e+04    3.23e+04    2.87e+05   2.64e+02   9.85e-01  1.23e+05       1    1.71e-01    8.66e-01
      4  1.803857e+04    5.58e+02    2.69e+04   8.66e+01   9.93e-01  3.69e+05       1    1.61e-01    1.03e+00
      5  1.803391e+04    4.66e+00    3.11e+02   1.02e+01   1.00e+00  1.11e+06       1    1.49e-01    1.18e+00

   Ceres Solver v1.12.0 Solve Report
   ----------------------------------
                                        Original                  Reduced
   Parameter blocks                        22122                    22122
   Parameters                              66462                    66462
   Residual blocks                         83718                    83718
   Residual                               167436                   167436

   Minimizer                        TRUST_REGION

   Sparse linear algebra library    SUITE_SPARSE
   Trust region strategy     LEVENBERG_MARQUARDT

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

   Cost:
   Initial                          4.185660e+06
   Final                            1.803391e+04
   Change                           4.167626e+06

   Minimizer iterations                        5
   Successful steps                            5
   Unsuccessful steps                          0

   Time (in seconds):
   Preprocessor                            0.283

     Residual evaluation                   0.061
     Jacobian evaluation                   0.361
     Linear solver                         0.382
   Minimizer                               0.895

   Postprocessor                           0.002
   Total                                   1.220

   Termination:                   NO_CONVERGENCE (Maximum number of iterations reached.)

Let us focus on run-time performance. The relevant lines to look at are


.. code-block:: console

   Time (in seconds):
   Preprocessor                            0.283

     Residual evaluation                   0.061
     Jacobian evaluation                   0.361
     Linear solver                         0.382
   Minimizer                               0.895

   Postprocessor                           0.002
   Total                                   1.220


Which tell us that of the total 1.2 seconds, about .3 seconds was spent in the
linear solver and the rest was mostly spent in preprocessing and jacobian
evaluation.

The preprocessing seems particularly expensive. Looking back at the report, we
observe

.. code-block:: console

   Linear solver ordering              AUTOMATIC                22106, 16

Which indicates that we are using automatic ordering for the ``SPARSE_SCHUR``
solver. This can be expensive at times. A straight forward way to deal with this
is to give the ordering manually. For ``bundle_adjuster`` this can be done by
passing the flag ``-ordering=user``. Doing so and looking at the timing block of
the full report gives us

.. code-block:: console

   Time (in seconds):
   Preprocessor                            0.051

     Residual evaluation                   0.053
     Jacobian evaluation                   0.344
     Linear solver                         0.372
   Minimizer                               0.854

   Postprocessor                           0.002
   Total                                   0.935


The preprocessor time has gone down by more than 5.5x!