Add dynamic_sparsity option.

The standard sparse normal Cholesky solver assumes a fixed
sparsity pattern which is useful for a large number of problems
presented to Ceres. However, some problems are symbolically dense
but numerically sparse i.e. each residual is a function of a
large number of parameters but at any given state the residual
only depends on a sparse subset of them. For these class of
problems it is faster to re-analyse the sparsity pattern of the
jacobian at each iteration of the non-linear optimisation instead
of including all of the zero entries in the step computation.

The proposed solution adds the dynamic_sparsity option which can
be used with SPARSE_NORMAL_CHOLESKY. A
DynamicCompressedRowSparseMatrix type (which extends
CompressedRowSparseMatrix) has been introduced which allows
dynamic addition and removal of elements. A Finalize method is
provided which then consolidates the matrix so that it can be
used in place of a regular CompressedRowSparseMatrix. An
associated jacobian writer has also been provided.

Changes that were required to make this extension were adding the
SetMaxNumNonZeros method to CompressedRowSparseMatrix and adding
a JacobianFinalizer template parameter to the ProgramEvaluator.

Change-Id: Ia5a8a9523fdae8d5b027bc35e70b4611ec2a8d01

examples/CMakeLists.txt

@@ -49,6 +49,9 @@ IF (NOT MSVC)
   TARGET_LINK_LIBRARIES(curve_fitting_c m)
 ENDIF (NOT MSVC)
 
+ADD_EXECUTABLE(ellipse_approximation ellipse_approximation.cc)
+TARGET_LINK_LIBRARIES(ellipse_approximation ceres)
+
 ADD_EXECUTABLE(robust_curve_fitting robust_curve_fitting.cc)
 TARGET_LINK_LIBRARIES(robust_curve_fitting ceres)