This eliminates an entire vector and computation of a square root
followed by a squaring.
Thanks to @rlabbe for pointing this out.
Change-Id: I0de117b31b9332c61e687f18466d7cb2e2ac611e
1. Add a version history
2. Update copyright years across the code base
3. Run format_all.sh
4. Update version strings from 2.1.0 to 2.2.0 in the docs and
elsewhere.
Change-Id: I46d8d479d54bd6002d532785e67342106e73c9ac
Disable the definition of `min`/`max` macros by defining `NOMINMAX`
and prevent macro substitution in the public interface.
Also, quiet floating-point comparisons are defined as template functions
by the MSVC STL which causes compilation errors due to ambiguities in
resolving the template parameter types.
Fixes#668Fixes#716Fixes#718
Change-Id: I5fe7832a6a3a7ad0421a2557527528c34b88e9c7
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
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
Migrate all Option and Summary structs to use
inline member initialization syntax.
This reduces the amount of code, and collocates the
default values with the documentation for the corresponding
member variable.
Change-Id: I8e6b9ee3b31464699d678667f6166ace5fc137c9
1. Default linear solver is Eigen::LDLT
2. Options::max_iterations -> Options::max_num_iterations
3. Options::error_threshold -> Options::cost_threshold
4. Options::relative_step_threshold -> Options::parameter_threshold
5. Options::initial_scale_factor -> Options::initial_trust_region_radius
6. The default values of the above parameters have been changed
to match those in ceres::Solver::Options
7. Status::RUNNING has been removed
8. Update now returns a bool instead of a Status enum and
the status handling has been included in the main loop.
9. Summary::gradient_norm has been changed to Summary::gradient_max_norm
to match the convergence test
10. A member variable cost_ has been added which is computed by Update
11. The test for parameter_tolerance based convergence is made
more robust near zero.
12. Use of double has been replaced by Scalar.
13. Minor clang-formatting
Change-Id: I3cb0e2fd0a0204476bb8718761dc740cdf5e42ce
The solver code must rely on the vectors for
sizing, since not all cost functions will have
NumParameters() or NumResiduals().
Change-Id: Id254ce37507443910edb0064de7907d64558851e
1. Default constructor and initialization for Summary.
2. Add Jacobi scaling.
3. Add bounds on the lm diagonal
4. Use the diagonal of J'J as the regularizer instead of identity.
5. Update the computation of rho to match the change in regularization.
As a result of these changes, the performance of TinySolver is
now the same as ceres::Solver, solving 53 out of 54 problems.
Change-Id: Ie08c3389ac2e3964ffa04411734c06b65835358a
1. Instead of Core/LU just include Eigen/Dense
2. Rename SolverParameters to Options and params to options.
3. Rename Results to Summary.
4. Summary::error_magnitude -> Summary::final_cost.
5. Add Summary::initial_cost.
6. Change definitions of Summary::initial_cost and Summary::final_cost
to match those used by Ceres::Solver.
Change-Id: Id64b78398f47810ca25938a15423c514fc8c164d
1. Change the ordering from NUM_PARAMETERS, NUM_RESIDUALS to
NUM_RESIDUALS, NUM_PARAMETERS in docs and in code.
2. TinySolver::solve -> TinySolver::Solve
Change-Id: I4dca87b971fd9168f1200b53c362669cffc82c1b
Tiny solver is targeted towards small dense least square
solves, where the overhead of calling normal Ceres is too
high. For example, when solving for inverse camera
distortion for every pixel location in a many-megapixel
image. Anecdotally, at one point in the past, tiny solver
was ~20x faster than Ceres for the problems it's intended
for. This is due to two key aspects:
1. Memory is allocated up front: repeated solves incur no
allocation overhead beyond a few scalars on the stack.
2. The cost function is fully inlined into the solver
loop, removing even the cost function call overhead.
Tiny solver originated many years ago as part of
libmv/Blender, where it is still used for distortion solving
today, but the time has come for it to migrate into Ceres.
This commit is just the initial import into Ceres. Follow
up patches will add further cleanups, and add CostFunction
and Jet adapters to make it easier to call tiny solver
(though by using adapters, some performance advantages will
be lost).
Change-Id: I8079535cd41382b1e0ac0ca2fca141711c72b7f8
Sameer Agarwal