- The intended use-case for these accessors is in client code tests to
support verification of the configuration with which cost functions
were constructed.
Change-Id: Ib77afa6409804ba7f724138f579e0c51b154f5ad
When computing the MinusJacobian we were passing two different
values to AutoDifferentiate for the output dimension. The template
argument was correct which is why the method was working correctly
but the function argument was incorrect (cut and paste error).
This would be fine in release mode, but in debug mode it would
trigger a check failure.
Change-Id: I0327656d1a4d34c82e4d3a8c04f27c264bce80eb
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
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
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
- Also adds documentation of mixed precision solves to Sphinx docs.
- Fix reference to Sphinx theme used (RTD not better).
- Fix NOTE syntax in use_explicit_schur_complement Sphinx docs.
Change-Id: I7bdac0f07eb737f49b05e3fcaa3eebd087355d2d
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
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
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
norm allows to compute the squared magnitude both of complex and real
numbers. While Jet does not support decaying of a std::complex to a
scalar performed by norm, the function is still useful when applied to
scalars alone for computing the square.
Change-Id: I27a3513f53f37fb3960411362e80d170d1ae6f74
libc++ (default on macOS) uses the copysign function for std::complex
multiplication which causes compilation errors such as these if
std::complex is combined with jets:
/Applications/Xcode_12.4.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/complex:607:19: error: no matching function for call to 'copysign'
__a = copysign(__libcpp_isinf_or_builtin(__a) ? _Tp(1) : _Tp(0), __a);
^~~~~~~~
foo.hpp:218:39: note: in instantiation of function template specialization 'std::__1::operator*<ceres::Jet<double, 3> >' requested here
std::complex<Scalar> result = lhs * rhs;
copysign is also useful in other situations where the sign is required
allowing it to compute without branching.
Change-Id: I84b2d23374f1bf3bae32833016bb686d97d74054
abs() implementation does not correctly handle the sign of the
infinitesimal part when the value is a NaN. Define the function in terms
of copysign which is the only portable way of manipulating the sign of
a NaN value.
Change-Id: I2f13fb1db62d35ca8b09e6bff1c05a736cb91513
This has been a long requested feature so that users can minimize
functions using numeric differentiation.
As part of this, I have also redone rosenbrock.cc, which now has three
variants.
rosenbrock.cc now uses automatic differentiation.
rosenbrock_numeric_diff.cc uses numeric differentiation.
rosenbrock_analytic_diff.cc uses analytic derivatives.
This is analogus to how the helloworld example code is structured.
The tutorial for GradientProblemSolver has also been updated to reflect
this.
https://github.com/ceres-solver/ceres-solver/issues/691
Change-Id: Ib0fb9e35127fe4c8299d4793bea3558722c70dd7
1. Add function_tolerance based termination, updating the
termination logic to be in line with Ceres.
2. Replace the use of "error" with "residuals" in the code.
Change-Id: I3fd543f3a8226fe7f07eeec358559cba934679b2
Comparison operators are defined for Jet-scalar and scalar-Jet.
Add corresponding overloads for fmax and fmix so users
don't have to convert to Jet only to access these functions,
plus it can potentially save an unnecessary conversion.
Change-Id: I20cc2d5874935e019d4ecb2d6a8c2ae631c8a4d3
This is needed to add a layer of indirection which will allow
us to use gflags in the public version and absl inside Google.
Change-Id: I32f3da23200a01c9b658bbf8aaa66cb8fddc2cc3
M_2_SQRTPI is not part of standard C++, replace it with an
equivalent expression. gcc immediately folds this and thus
generates the same code even at -O0.
Change-Id: I5d38e67a4508c1d43a1a8e5f1639f2243cdb143e
- ensure all public headers files adhere to clang-format
- preserve one-per-line for enums by adding trailing comma
- preserve include order for en/disable_warning.h
Change-Id: I78dbd0527a294ab2ec5f074fb426e48b20c393e6
1. Add a move constructor to NumericDiffCostFunction, DynamicAutoDiffCostfunction
and DynamicNumericDiffCostFunction.
2. Add optional ownership of the underlying functor.
3. Update docs to reflect this as well as the variadic templates that allow an
arbitrary number of parameter blocks.
Change-Id: I57bbb51fb9e75f36ec2a661b603beda270f30a19
Add Ownership semantics to the AutoDiffCostFunction
This allows several benefits, such as pointer ordering always being the
same for numerical repeatability (due to blocks being ordered by
pointer address), memory adjacency for better cache performance, and
reduced allocator pressure / overhead.
This is then made use of in libmv by preallocating the errors and
cost functions into vectors
Change-Id: Ia5b97e7249b55a463264b6e26f7a02291927c9f2
erf is necessary for evaluating Gaussian functions.
erfc was added because it is so similar to erf.
Change-Id: I5e470dbe013cc938fabb87cde3b0ebf26a90fff4
Simplify the semantics for Problem::EvaluateResidualBlock to
not ignore the presence of EvaluationCallback and add another method
EvaluateResidualBlockAssumingParametersUnchanged to handle the case
where the user has an EvaluationCallback but knows that the parameter
blocks do not change between calls.
Updated the documentation for the methods and EvaluationCallback to
reflect these semantics.
Also added tests for Evaluation related methods calling i
EvaluationCallback when its present.
https://github.com/ceres-solver/ceres-solver/issues/483
Change-Id: If0a0c95c2f1f92e9183a90df240104a69a71c46d
Alex Stewart