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This has been replaced by two enums now.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
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
1. Multithread the inversion of J'J.
2. Simplify the dense rank truncation loop.
3. Minor correction to building documentation.
Change-Id: Ide932811c0f28dc6c253809339fb2caa083865b5
1. Further tightening of the Covariance documentation.
2. Documented minimizer progress output.
3. Lint cleanup from William Rucklidge.
4. Updated version history.
Change-Id: I8bc28484675d4edf89a7c050b6379dbac6c39e91
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
This allows CovarianceImpl to be forward declared without
scoped_ptr freaking out.
Thanks to Nima Keivan for reporting this.
Change-Id: Icd5aa766b3aab70246055225231a4b971c6b7b90
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