Applied changes correspond to clang-tidy fixes
stemming from the modernize-use-equals-default check.
Change-Id: I254b0908a76d464131564b637cd0e42a6b03fb5a
virtual can be ambiguous. Applied changes correspond to clang-tidy fixes
stemming from the modernize-use-override check.
Change-Id: I973afd4680a5df587419777504aeb94467196b89
1. Add CUDADenseCholesky64Bit, CUDADenseCholesky32Bit, & tests.
CUDADenseCholesky32Bit uses the legacy versions of potrf/potrs
in cuSolverDN, while CUDADenseCholesky64Bit uses the new 64-bit
versions available since Cuda 11.1. The legacy versions are
provided since some platforms such as the Nvidia Jetsons only
support Cuda 10.2.
2. Expose CUDA as a new option under DenseLinearAlgebraLibraryType.
The relevant option to string and string to option helper functions
are modified accordingly.
3. Add cuda as a dense_linear_algebra_library option in bundle_adjuster
to demonstrate the use of the new CUDA option.
Change-Id: I23615e1d301df5185ed646b3e33ee802508dae86
This MR adds SphereManifold ported from
HomogeneousVectorParameterization. Additionally the minus operator
and jacobian evaluation was implemented.
The unit tests were almost completly reimplemented and uses the
test facilities provided for manifolds.
Change-Id: Iccf72a2333bc921ff24c4d831db35020c653ee86
Complete support for all floating-point classification functions
(fpclassify, signbit) and consistently apply all overloads recursively
to the scalar part of a Jet only. This is now inline with how comparison
operators work. Sanity checks of derivatives should be performed
explicitly on the dual part of a Jet due an ambiguity on reducing the
classification results of multiple values.
Provide an fdim overload (in addition to fmin and fmax) and support
quiet versions of comparison operators also applied recursively to the
scalar part of a Jet but without type promotion.
Additionally, deprecate Ceres legacy classification functions. New code
should use C++11 function names for consistency.
Finally, simplify expressions using introduced scalar classification and
comparison.
Change-Id: I397e37425760717b991eb7ae5da0892f20c5a365
1. Add EigenDenseQR & tests.
This implementation now uses an in place decomposition,
which means that we are not allocating, deallocating
memory every call.
2. Add LAPACKDenseQR and tests.
The LAPACK implementation instead of using dgels which is a
routine which does the factorization and solve in one
call, now uses dgeqrf for factorization and then
dormqr and dtrtrs for solving. This allows us to
have a factorize and solve interface like DenseCholesky.
And opens the door to iterative refinement and mixed
precision solves.
3. The refactor also allows us to simplify the interface to
DenseSparseMatrix considerably. The internals of this
class were complicated because we had the AppendDiagonal
and RemoveDiagonal methods and we did not want to allocate
deallocate memory every call. But since we pay the cost
of the copy anyways, we can just hold that buffer
in DenseQRSolver.
4. Delete lapack.cc/h
5. The net result is that everything seems to be a bit faster.
For LAPACK we are not doing some of the scaling work that
dgels was doing. For Eigen I think it maybe the inplace
decomposition.
Benchmark Time CPU Time Old Time New CPU Old CPU New
----------------------------------------------------------------------------------------------------------------------------------------------------------
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/1/1 -0.1154 -0.1159 692 612 691 611
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/2/1 -0.1601 -0.1553 717 603 712 601
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/3/1 -0.1673 -0.1575 733 610 724 610
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/6/2 -0.1008 -0.1003 886 797 884 796
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/10/3 -0.1489 -0.1514 1283 1092 1281 1087
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/12/4 -0.1040 -0.1104 1556 1394 1553 1381
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/20/5 -0.0007 -0.0097 1911 1910 1908 1890
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/40/5 -0.1033 -0.1022 2981 2673 2957 2655
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/100/10 -0.0147 +0.0015 9275 9138 9026 9040
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/200/10 -0.1408 -0.1284 15093 12968 14778 12880
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/200/20 -0.0310 -0.0355 38973 37765 38837 37460
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/1/1 -0.1228 -0.1256 736 646 731 640
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/2/1 -0.1401 -0.1396 740 636 735 633
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/3/1 -0.1731 -0.1695 744 615 738 613
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/6/2 -0.1399 -0.1408 1121 965 1113 956
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/10/3 -0.1110 -0.1145 1571 1397 1560 1382
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/12/4 -0.1411 -0.1417 2006 1722 1993 1710
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/20/5 -0.1740 -0.1729 2741 2264 2724 2253
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/40/5 -0.0966 -0.1123 3462 3128 3425 3040
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/100/10 -0.0387 -0.0998 10365 9964 10339 9307
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/200/10 -0.2044 -0.2049 16031 12754 15998 12720
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/200/20 -0.2391 -0.2386 35777 27223 35716 27193
Change-Id: I782f0d7664efe1435eebda92ddf47a0fe66c9c72
This is needed to make the dense_linear_solver_benchmark.cc
compile with the currently stable versions of the benchmark
library available on various linux distributions.
Change-Id: I1e391d5c2d16250d213bcfa3d50f9560aad9a363
Like SparseCholesky, the DenseCholesky interface abstracts
away the solution of dense linear systems using Cholesky factorization.
This allows the client code to not worry about the type of dense
linear algebra library being used.
DenseNormalCholeskySolver and DenseSchurComplementSolver code
is considerably simpler as a result.
Change-Id: Ie15f09ee376d5f9a64609e6a55ad83e99c76352a
AutoDiffManifold allows the user to define a templated
functor that implements the Plus and Minus operations
on the Manifold and will compute the Jacobians needed
to define the Manifold object using automatic differentiation.
Change-Id: Ibd073c25847389308ca1ab66e6f5fe78aae77205
Manifolds are now part of the public API and co-exist
with LocalParameterizations.
1. Add Manifolds to the Problem API.
a. AddParameterBlock(double*, int, Manifold*)
b. SetParameterization(double*, Manifold*)
b. GetManifold(const double*)
c. HasManifold(const double*)
2. Internally Ceres now only uses Manifolds. When the user uses
a LocalParameterization, it is wrapped in a ManifoldAdapter.
3. To preserve the API semantics while keeping the internals clean
we need a new map in ProblemImpl which stores the association
between parameter blocks and local parameterizations. This
is temporary, it will go away once this transition is complete.
4. There are NO algorithmic changes, as in we are not using
any of the expanded interface of the Manifold objects yet.
That will come later.
5. All tests that use LocalParameterization have been duplicated
to use Manifolds, and when this transition is complete the
LocalParameterization based tests will be deleted.
6. Public documentation for the API has been updated. Deprecation
notices to the documentation as well as C++ annotations will come
later.
7. Similar changes have been made to GradientProblem.
Change-Id: I8e03c8ced6e141876ef3eca5740c113afa788f0c
1. Break tests for one function into its own TEST instance.
2. Use a gmock matcher which gives better logging, previously
we were using a function which in turn used another function
in test_utils. It made tracking down the failing test very
hard.
3. Disable a hypot3 underflow and overflow test when using
libc++ as the three argument in libc++ is borked.
4. The use of the matcher has made a number of comments redundant
so I have removed them.
This is the first step in cleaning up these tests and then expanding
them.
Change-Id: Ib0c827f44432e2496e50b8cda3e06c26bdf50f08
This will help the transition from LocalParameterization to Manifolds,
since most uses of Problem::GetParameterization is to just check
whether a parameter block has a local parameterization associated
with it or not.
Change-Id: Ib3539f377eaed853d7542c9844ec1487aa0fb4d6
1. Increase number of trials.
2. Make all the matchers per point.
3. Add matchers for delta = 0.
4. Add a macro which invokes all the matchers, reducing boilerplate
Change-Id: Ia1bf110323c5877a1b92aef34c12c39008256f05
This is the first in a series of changes that will eventually
replace the LocalParameterization interface with the richer
Manifold interface.
1. Add the Manifold interface.
2. Add implementations and test for:
a. EuclideanManifold (formerly the IdentityParameterization)
b. SubsetManifold (formerly the SubsetParameterization)
c. ProductManifold (formerly the ProductParameterization)
The testing has been completely re-done, where instead of adhoc
testing, we now define a number of matchers which explicitly
enforce the invariants demanded by the Manifold interface.
Change-Id: I3f296d0964388d52b027c99dc86b7730d24d55fa
Previously, Jet did not allow comparison between its value and a scalar
of another type. Specifically, the comparison was limited to scalars of
the same type effectively disabling standard promotion rules. Instead,
users would be required either to cast values to target Jet arithmetic
type or explicitly construct a Jet instance that can be eventually used
for comparison purposes.
However, such a behavior is not intuitive and causes problems with types
that rely on standard promotion rules (e.g., std::complex in some
implementations).
This changeset extends the comparison operators by enabling logical
comparisons between a Jet and values compatible to the underlying scalar
type (e.g., int). To be as generic as possible, the types allowed to be
passed to comparison operators are constrained using SFINAE. This in
turn allows a recursive expansion of jets and therefore the comparison
of a nested jets with a scalar.
The changes alleviate problems described in #414 and also allow the use
of special functions from Boost.Math, e.g., for computing the reciprocal
using boost::math::pow<-1>(...).
Change-Id: I3625791e6c8c2c0bfbffbbdb09e179a32e57f306
Several std::allocator<> members were deprecated in C++17 and
subsequently removed in C++20. Drop deprecated members altogether since
these are unused anyway.
Change-Id: Ic26471a4b1cb3ccc1fdf3231400c81827b0ff712
C++17 provides a three argument hypot(x, y, z) which can now be used
for jets if the standard is active.
Change-Id: Ide62e101f780fe738bb2d4f826b10daf94c585b3
Eigen::MappedSparseMatrix has been deprecated and removed from
Eigen at HEAD.
Thanks to rmlarsen@ for fixing this.
Change-Id: I34f3b0dda2bb91ee8cc65a20e53a3d7de6929221
Zero sign propagation is not always guaranteed. Replace signbit checks
by checks for -nan and +nan sign.
Change-Id: If2ba367e4116d4e6a008a83cf2bc6a8851b27af7
fmin and fmax do not handle NaNs correctly. Also, the comparison
operator for floating-point numbers may raise FE_INVALID if one of the
arguments is NaN. Both functions, however, are not subject to any of the
error conditions specified by the error handling for related
floating-point operators and functions.
Change-Id: Ic6bb65f18568066dba3c739a2df06f5fc3131a80
Currently, it is not possible to accurately evaluate the derivative of
d/dx log(1 + x) under all circumstances and significant deviations from
the actual derivative d/dx log1p(x) can occur. This changeset introduces
the necessary Jet overload and its inverse, expm1.
Change-Id: Ifcf88f6d684f61ba86bbe49f0d551b703f34ad0d
Sergiu Deitsch