This header defined integral types in the pre-C++11 days, and can
be replaced with <cstdint> and the types defined therein.
Also remove a shallow (and incorrect) typedef in include/ceres/types.h
https://github.com/ceres-solver/ceres-solver/issues/409
Change-Id: I398c652f74d24bbeea459672508bf28f591b100f
1. Replace HashMap and HashSet with std::unordered_map and
std::unordered_set respectively.
2. Extract the pair hasher into a struct pair_hash.
3. Delete collections_port.h
4. Convert explicit iterator based loops to auto based
loops where sensible.
Change-Id: Ib88bcd13a7463d18435639d3b771abaa52080efb
THIS IS AN API BREAKING CHANGE.
Decouple the algorithm from the sparse linear algebra
library being used to perform the computation.
Before this change
Covariance::AlgorithmType had values
DENSE_SVD
EIGEN_SPARSE_QR
SUITE_SPARSE_QR
This has been replaced by two enums now.
Covariance::Options::sparse_linear_algebra_library_type
which can take values EIGEN_SPARSE, SUITE_SPARSE or CX_SPARSE.
The last one is currently not supported.
And Covariance::Options::algorithm_type takes values
DENSE_SVD
SPARSE_QR
This sets the stage for future extensions of the covariance
computation algorithm.
Also as part of this change, the covariance computation chapter
has been made a top level chapter on its own instead of being
buried deep inside the Solving Non-linear Least Squares problem.
Change-Id: Ibfbf60902d8d17694d9ff585047a5a57d329ab22
Computing the covariance matrix for a number of parameter blocks
previously required adding all parameter blocks to the computation and
subsequently assembling the matrix by concatenating all the blocks.
This patch adds the computation of the covariance matrix for a vector
of parameter blocks. All covariance block pairs are added automatically
and the resulting covariance matrix is assembled in the order the
parameter blocks appear.
Change-Id: I3b70c63f16862adc23a1d7fb7a21dde4e68abe9a
Parameter blocks that are not associated with any residual block
lead to structurally zero columns in the Jacobian. The covariance
computation algorithm was only paying attention to structural
sparsity caused by constant parameter blocks but not free parameter
blocks.
This patch fixes this, by iterating over the residual blocks in
the problem and collecting all the parameter blocks in use.
The tests for ComputeCovarianceSparsity are also extended to include
the case where there are constant and free parameter blocks.
Thanks to Wannes Van Loock for reporting this.
Change-Id: Ic298a6e93c53f2f95fb69105397a87200738a2b0
This commit fixes a bug related to the computation of covariance blocks
in the tangent space for constant parameter blocks, causing out of
bounds memory access.
Change-Id: Iaeee7992405fcaaae6086612798e96f2e10ebc5c
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
This CL is required to build Tango.
Inspired by this commit in RedwoodInternal repository:
commit 09dde53c248e04f432b5eccceea5daeedb706aea
Author: Mike Vitus <mike@hidof.com>
Date: Wed Apr 23 11:05:17 2014 -0700
Change-Id: I328b6634969de4ccdd71947945aa67a49ee9073f
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
Sparse Cholesky factorization is not rank revealing. Therefore
this algorithm cannot reliably tell when the Jacobian matrix is
rank deficient or so poorly conditioned that the covariance matrix
cannot be estimated.
Making things worse, this algorithm works on the normal equations,
which makes the conditioning problem much worse.
This change, deletes the SPARSE_CHOLESKY algorithm in the covariance
estimation code. Also to make the naming consistent, it renames
SPARSE_QR -> SUITE_SPARSE_QR
so that it parallels EIGEN_SPARSE_QR.
Also, since we now have EIGEN_SPARSE_QR, we can default to using
it when SuiteSparse is not available instead of DENSE_SVD, which
generally speaking should only be used by folks who are dealing
with small rank deficient jacobians.
Change-Id: I8b134c7e8a2e86ca374371f185b19f1c3e74349c
For smaller problems Eigen is faster than SuiteSparseQR. This has been
tested with Eigen 3.2.1. Below are detailed timings. Problem 1 is the
smallest and problem 3 is the largest. The timings below are:
mean +- standard deviation.
Problem 1:
Eigen 0.0009218 +- 0.0002755
SuiteSparse 0.001406 +- 0.001610
Problem 2:
Eigen 0.002338 +- 0.001005
SuiteSparse 0.001910 +- 0.0004513
Problem 3:
Eigen 0.005455 +- 0.001759
SuiteSparse 0.002411 +- 0.0004974
Detailed problem descriptions:
Problem 1 size:
Original Reduced
Parameter blocks 533 54
Parameters 368 104
Effective parameters 1201 94
Residual blocks 233 77
Residual 1194 258
Problem 2 size:
Original Reduced
Parameter blocks 573 84
Parameters 1458 184
Effective parameters 1281 164
Residual blocks 263 107
Residual 1314 378
Problem 3 size:
Original Reduced
Parameter blocks 613 114
Parameters 1548 264
Effective parameters 1361 234
Residual blocks 293 137
Residual 1434 498
Change-Id: I884a67e2f728fe2992812148d82ccf5f27864fd7
CostFunction now uses int32 instead of int16
to store the size of its parameter blocks.
This is an API breaking change.
Change-Id: I032ea583bc7ea4b3009be25d23a3be143749c73e
1. Rename SolverTerminationType to TerminationType.
2. Consolidate the enum as
a. CONVERGENCE - subsumes FUNCTION_TOLERANCE, PARAMETER_TOLERANCE and GRADIENT_TOLERANCE
b. NO_CONVERGENCE
c. FAILURE - captures all kinds of failures including DID_NOT_RUN.
d. USER_SUCCESS
e. USER_FAILURE
3. Solver::Summary::error is renamed to be Solver::Summary::message, to both
reduce confusion as well as capture its true meaning.
Change-Id: I27a382e66e67f5a4750d0ee914d941f6b53c326d
When using the SPARSE_QR algorithm, now a Q-less
factorization is used. This results in significantly
less memory usage.
The inversion of the semi-normal equations is now
threaded using openmp. Indeed if one has SuiteSparse
compiled with TBB, then both the factorization
and the inversion are completely threaded.
Change-Id: Ia07591e48e7958d427ef91ff9e67662f6e982c21
1. When protocol buffers support is enabled CompressedRowSparseMatrix
has a missing virtual method.
2. When SuiteSparse is missing, covariance_test tries to run the
large scale covariance computation test.
Thanks to Alex Stewart for reporting #1.
Change-Id: I4238c966036362175e31749595ea8bb6f12a696c
1. Multithread the inversion of J'J.
2. Simplify the dense rank truncation loop.
3. Minor correction to building documentation.
Change-Id: Ide932811c0f28dc6c253809339fb2caa083865b5
1. Sparse covariance estimation now uses cholmod_rcond to
detect singular Jacobians.
2. Dense covariance estimation now uses relative magnitude
of singular/eigen values to compute the pseudoinverse.
3. Truncation logic is now unified with Solver::Options::null_space_rank.
Change-Id: I095bd737510c836b4251255926190a7f31d64bce
Add a Covariance object to the API.
Given a Problem object and a set of parameter block pairs the
Covariance object computes a sparse covariance matrix corresponding
to those block pairs and provides random access to them.
Constant parameter blocks and parameter blocks with local parameterizations
are correctly handled.
Sparse and dense implementations are provided. With the dense implementation
rank deficient Jacobians can also be handled.
Parts of the code are threaded using OpenMP if available.
Change-Id: I5b49583b3d79579df3e0f334c22567acb23ed4ad
Sameer Agarwal