1. Generalize SuiteSparse::AnalyzeCholesky and
SuiteSparse::BlockAnalyzeCholesky from just doing AMD to taking
OrderingType as an argument and using that to determine whether
AMD & Nested Dissection algorithms are used for computing the
fill-reducing ordering or a natural ordering when computing
the symbolic factorization.
2. Remove AnalyzeCholeskyWithNaturalOrdering.
3. Replace and generalize SuiteSparse::BlockAMDOrdering with
SuiteSparse::BlockOrdering which also takes OrderingType as an
argument. Same for SuiteSparse::ApproximateMinimumDegreeOrdering
and SuiteSparse::NestedDissectionOrdering by
SuiteSparse::Ordering.
4. Remove LinearSolver::Options::use_postordering and replace it
with LinearSolver::Options::ordering_type.
5. Replace Preconditioner::Options::use_postordering and replace it
with Preconditioner::Options::ordering_type.
6. Add NESDIS to OrderingType. With the above changes, the linear
solvers can now use Nested Dissection once this information
is piped through the nonlinear solver.
Change-Id: Ib8e93fbf34ae2981bf2ac54dcda9e25c7c213790
With this change we can drop the complicated/conditional handling
around CAMD and assume that it is always available.
Change-Id: I93e1da676fb75817f79824b8b2b6549d03f278b0
* Ubuntu 18.04 GCC does not fully support C++17, hence remove the
runner.
* Using CMake SuiteSparse in a C++17 project requires a workaround
implemented in a recent release.
Change-Id: I9985fe12d582dfc9b74e97d670828334e507e9f5
Instead of having four separate scalars, allocate them as
an array as they are all touched as a group of four.
Change-Id: I773cfc08cf53b66032985c11a4b0ebc06db06083
1. Update the cff file to be more inclusive.
2. Update the BibTeX entry in index.rst to match the one generated
by GitHub.
Change-Id: I26d031b2128d1d4330623bcdace284ca9ffce9e1
The last item in the 'New Features' section restarts the enumeration
because the indention of the previous subitem is one space short.
Change-Id: Ifa2873d3e2ddd6bac5034b48207775019ef1c462
This makes citations accessible directly on Github and allows users to
automatically generate citations in APA and BibTeX format.
Change-Id: Ic5eb3857c92d93a6afafba06a5009da5db2b3c60
Compiling jet_test using the /std:c++17 switch triggers a C3198 compile
error in <numeric>. Moving #pragma below all the includes, allows to
workaround the issue.
Additionally, locally ensure the floating-point model is always
/fp:precise to be able to access the floating-point environment in
jet_test.
Change-Id: Ia5b3a3dac13baf46546ac1d0d304fc05512f8816
If SuiteSparse is found, an unhelpful message "Found SuiteSparse: TRUE
..." is printed. Instead, report the found include directory and version
information which was previously not shown due to unset
SuiteSparse_FOUND variable.
Change-Id: Ib43fb99934f34e6007110007d2cd4a8fbd841aa2
pair_hash.h uses std::size_t and std::hash but does not include the
corresponding headers <cstddef> and <functional>.
Change-Id: I194a5c76e8f50b1574e1359f616351581033c576
* Use generator expression instead of CMAKE_RUNTIME_OUTPUT_DIRECTORY
to get the path of compiled CUDA test targets when running
cuda-memcheck tests.
* Only add cuda-memcheck targets if testing is enabled.
Change-Id: Idea498dd9008b7e5075d4af9775f9f43716e22f1
covariance.h was using SUITE_SPARSE even when SUITESPARSE
was disabled because it did not have config.h included in it
so it did not see that CERES_NO_SUITESPARSE was defined.
Add more config.h includes to files that are using these
configuration macros.
Change-Id: I6b1d2c2bd9e559de40a6332cd6be85ad4da3377b
A recent change introduced some uses of `IN_LIST` in
FindSuiteSparse.cmake, but this is only introduced in cmake 3.3 and
breaks downstream projects that set cmake_minimum_required() to anything
lower.
This commit locally sets CMP0057, which enables the `IN_LIST` operator
and fixes the build for these projects.
Primarily motivated by colmap, which sets cmake_minimum_required(3.0)
and is currently broken: https://github.com/colmap/colmap/issues/1451
Change-Id: I9580c86f56248611326a932b8650b9048fb0ff14
In the case, necessary properties of import targets cannot be set (i.e.,
either because the include directory or the library was found), do not
define import target to begin with.
Change-Id: Id216cd692a8ec240a20f65b174f196ddaa306c2b
Overriding export gflags export macros breaks glog in shared Ceres
solver builds. Threfore, always compile gtest as a static library to
avoid the need of overriding the export macros.
Change-Id: Ibc9a04a771085caa8f02c81745ce626643df8450
These changes allow the use of a SuiteSparse CMake package from
https://github.com/sergiud/SuiteSparse that allows native compilation of
SuiteSparse using CMake on a variety of platforms Packages generated
using official SuiteSparse makefiles can still be used without
modifications. The find module remains agnostic to specific CMake
package implementation.
CMake packages have the advantage that they are self-contained and
relocatable. The latter is particularly useful in cross-compilation
scenarios.
Fixes#728
Change-Id: I089d5c6f87c05b1530a5ab9a36dff2fcbe82d13d
In many cases, manifolds stored in ProductManifold have a default
constructor which can simplify ProductManifold initialization even
further. Allow default construction of ProductManifold in this case.
Change-Id: I29b2612870c02232556688019a77049709684a55
* Fix workspace type in CUDADenseQR and CUDADenseCholesky --
Workspace sizes are in terms of number of elements, not bytes.
* Add cuda-memcheck tests to catch such CUDA memory errors in
the future.
Change-Id: I3dd0f0947daba9e4c6cd0216bef81d694547d505
Since the number of manifolds used to initialize ProductManifold and
their types are known at compile-time, it is possible to avoid storing
pointers to the base class as required by a homogeneous, currently
dynamically sized container. Instead, we can use std::tuple<> as a
heterogenous container with the number of elements fixed at compile-time
that allows us to store the concrete manifold realizations.
The advantage of this approach is that we can bypass the vtable when
iterating over each manifold within ProductManifold. The indirection is
invoked only once while accessing the ProductManifoldImpl members.
Additionally, potential dynamic memory allocations by a std::vector can
be completely avoided. This makes the ProductManifold implementation
more efficient both in memory and runtime.
Change-Id: Ic71b0c175ab726f8992e9703f7666bca477baf19
Previously they were defined in manifold.h but their implementations
were in the internal directory and to prevent circular dependencies
the implementation headers were pushed to the bottom of manifold.h
This started out as one header and has become progressively worse
as more manifolds are templated.
This change moves the two manifolds into their own headers which
also contain their implementations.
Change-Id: I671da0279a47cd2ff1f52c69a1d159426f55bd80
Sameer Agarwal