Currently, the logic for exporting symbols is rather complicated: when
tests are enabled internal symbols are exported in addition to the
public symbols. Such logic causes several problems. (1) Test binaries
link against a Ceres build that is different from the final release
since fewer optimizations are applied if more symbols are exported. (2)
Also, some toolchains hide symbols by default breaking the existing
logic eventually causing linker errors.
Since internal symbols are not intended to be used outside of the
project, we can compile them into object files and use exactly the same
binary code both for the final build and the tests without relying on
conditionals.
By default, all symbols are now hidden unless annotated as public.
Internal symbols are explicitly marked as not being exported in case
users chose not to hide symbols by default.
Change-Id: I589dd10be2f6f438508783cf99d141af0120057b
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
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
- In C you're not allowed to define variables in the middle
of the block. This was violated in curve_fitting.c by
calling ceres_init() in the beginning of main() and declaring
variables later.
- Also ifdef-ed suitesparse stuff in covariance estimation module.
This solves compilation error when you don't have suitesparse
compiled/installed.
Change-Id: I22b543c09ea01f55e127079daade99a0b781f789
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
Sergiu Deitsch