- 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
DynamicAutoDiffCostFunction::Evaluate when provided with a jacobians
array that was non-empty but all its entries are nullptr, would
compute num_active_parameters = 0, and then skip over all the loops
that evaluated the CostFunctor.
The fix is to check if num_active_parameters == 0, and then treat
it as the case where jacobians array is null.
Thanks to Ky Waegel for reporting and providing a reproduction for this.
Change-Id: Ib86930c2c3f722724d249f662bf88238679bbf98
Now that we are using c++11, it is safer to use override in subclasses
instead of virtual.
This CL does it for the interface, a follow up CL will do it for
other parts of the code base.
Change-Id: Ice8d0f4355cb700019d7a9c1566fbff0099e97d6
For proper alignment on the heap Eigen needs to have a custom
allocator. There are two forms, new and in-place new. To make sure
that memory is aligned using new, one needs to overload new by
adding EIGEN_MAKE_ALIGNED_OPERATOR_NEW to any struct which contains a
fixed size Eigen type either through inheritance or as a direct or
indirect member. For the in-place new one need to use the
Eigen::aligned_allocator (e.g. for std::vector, std::list,
FixedArray, etc.). For more details see:
https://eigen.tuxfamily.org/dox/group__DenseMatrixManipulation__Alignement.html
This CL adds EIGEN_MAKE_ALIGNED_OPERATOR_NEW to all structs, which
contain fixed-size Eigen types and uses the Eigen::aligned_allocator
for containers which stores structs of fixed-size Eigen types.
Change-Id: I06c6c4fc74a6835918d5d1c571b7814a14c029d8
In the process also move some documentation from the file header to
just above the class declaration.
This change is in response to https://github.com/ceres-solver/ceres-solver/issues/233
Change-Id: I08cff1a94c57b67dd2bd8be4dba8c3fce46b68ab
Since Ceres is moving to using GitHub for issues, and the Google
Code URL in the current copyright header will soon become invalid,
update all the headers.
Change-Id: I1fce70375d1bcf098591f07b4d8f01a5c1e0789c
For historical reasons we had a "using namespace std;" in port.h. This
is generally a bad idea. So removing it and along the way doing a bunch
of cpplint cleanup.
Change-Id: Ia125601a55ae62695e247fb0250df4c6f86c46c6
This brings the ability to have numerically differentiated
cost functions to be added with its structure decided on
runtime rather than compile time.
And some minor cleanups.
Two things still need to be done.
a. Update the modeling docs.
b. Remove RuntimeNumericDiffCostFunction in ceres::internal
and replace its usage with DynamicNumericDiffCostFunction.
Change-Id: Ib771f093f29236c95a99df31c584d579b8e36615
Changed DynamicAutoDiffCostFunction to handle multiple derivative
sections as opposed to just a single contiguous block.
In the previous implementation it was assumed that non-constant
parameters occur in a single contiguous block so that constant
parameters could NOT lie between non-constant parameters. Previously,
start_derivative_section was first set as soon as the first
non-constant parameter block (marked by jacobians[i] != NULL) was
encountered. After this, entries in input_jets[parameter_cursor].v were
accessed with `parameter_cursor - start_derivative_section`. For
contiguous non-constant parameter blocks this is fine, but if constant
parameter blocks fall between then this indexing is incorrect because
`parameter_cursor - start_derivative_section` can go out of bounds.
For a concrete example, take a cost function with three parameter
blocks, each of size 1 and with the center block fixed. Assume that
Stride=1 so that two passes are required. On the first pass
start_derivative_section=0, and the first variable block is handled
correctly. At the end of the first pass end_derivative_section=1, so
for the second pass start_derivative_section=1. Now comes the problem.
When parameter_cursor=1, parameter_cursor >= start_derivative_section
so jacobian[1] is checked to be NULL. Since it is NULL (second
parameter block is constant) then nothing is done and
active_parameter_count is NOT incremented. Next, when
parameter_cursor=2, parameter_cursor >= start_derivative_section and
jacobian[2] is checked. Since it is not NULL then
input_jets[parameter_cursor].v[parameter_cursor -
start_derivative_section] is set to 1.0, BUT parameter_cursor -
start_derivative_section = 2 - 1 = 1 which is out of bounds
(input_jets[parameter_cursor].v is only of size Stride=1).
The proposed solution records the start of each contiguous block of
non-constant parameters and indexing into
input_jets[parameter_cursor].v is independent of parameter_cursor.
Change-Id: I388ab6a0bafa35d317491135ec6fe980453ff888
Nikolaus Demmel