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
We now require that the first derivative of the loss function
be positive only if the second derivative is non-zero. This is
because when the second derivative is non-positive, we do not use
the second order correction suggested by BANS and instead use
a simpler first order strategy which does not use a division by
the gradient of the loss function.
Change-Id: I3d65713f152611998e196ff389a7081acfdfd8c1
Since we added special handling for the case for rho[2] < 0,
the bulk of CorrectJacobian is pointless in the common case.
So add a simple one dimensional loop which rescales the Jacobian.
This speeds up this method immensely.
The robustification of a Jacobian gets speeded up by > 50%.
Change-Id: I97c4e897ccbb5521c053e1fb931c5d0d32f542c7
Remove the Eigen temporary by revealing the columnwise nature
of the computation. This also allows us to get rid of the
special case for nrow = 1.
On problem-356-226730-pre.txt with -robustify evaluation times
change from:
Before:
Residual Evaluations 1.015
Jacobian Evaluations 18.313
After:
Residual Evaluations 1.005
Jacobian Evaluations 8.382
To give a sense of the overhead reduction, compare these numbers
when loss functions are disabled.
Residual Evaluations 0.955
Jacobian Evaluations 7.772
So, this is a 17.5x speedup!
The one dimensional specialization was motivated by denoising.cc.
The evaluation times there are essentially unchanged.
Before:
Residual Evaluations 2.774
Jacobian Evaluations 20.178
After:
Residual Evaluations 2.588
Jacobian Evaluations 19.781
Change-Id: Ic0efbaed75fe4489635039f17189ae24b97802c8
Add a specialization for the common case where the residual block
outputs exactly one residual.
The matrix routines used by Corrector can be then specialized to
a scalar and be made considerably faster.
For denoising upto 400% speedup is observed.
Change-Id: I8e3f24b8ba41caa8e62ad97c5f5e96ab6ea47150
Kuang Fangjun