hanged my algorithm
>>>> slightly and now only need .pow(), but this does not work with Jets either.
>>>> I think the problem with .pow() looks easier to fix?
>>>>
>>>>
>>>>
>>>> Thanks,
>>>>
>>>> Toby
>>&
o, I have changed my algorithm
>>> slightly and now only need .pow(), but this does not work with Jets either.
>>> I think the problem with .pow() looks easier to fix?
>>>
>>>
>>>
>>> Thanks,
>>>
>>> Toby
>>>
>>>
>&g
Oleg,
would you mind posting the ceres issues either on the ceres mailinglist or
on https://github.com/ceres-solver/ceres-solver/issues.
Sameer
On Mon, Jun 1, 2020 at 8:44 AM Oleg Shirokobrod
wrote:
> Hi Sameer,
>
> Thank you for quick reply and advices.
> 1. I implemented using both
>> *Reply to: *"eigen@lists.tuxfamily.org"
>> *Date: *Thursday, 4 June 2020 at 18:46
>> *To: *eigen , Sameer Agarwal <
>> sameeragar...@google.com>
>> *Subject: *Re: [eigen] Using Eigen decompositions with ceres autodiff
>> Jet data typ
18:46
> *To: *eigen , Sameer Agarwal <
> sameeragar...@google.com>
> *Subject: *Re: [eigen] Using Eigen decompositions with ceres autodiff Jet
> data type.
>
>
>
> Hi Tobias,
>
> Please do. Sameer, since this is Ceres solver related, could I ask you to
> help
: Thursday, 4 June 2020 at 18:46
To: eigen , Sameer Agarwal
Subject: Re: [eigen] Using Eigen decompositions with ceres autodiff Jet data
type.
Hi Tobias,
Please do. Sameer, since this is Ceres solver related, could I ask you to help
out with this issue.
Rasmus
On Thu, Jun 4, 2020 at 3:06 AM Wo
Rasmus,
I expected that ceres-solver traits do the job
#if EIGEN_VERSION_AT_LEAST(3, 3, 0)
// Specifying the return type of binary operations between Jets and scalar
types
// allows you to perform matrix/array operations with Eigen matrices and
arrays
// such as addition, subtraction,
Oleg,
Basically my response is that A.jacobiSVD().solve( b.cast()) is what
you should be doing - we made a conscious decision to disallow code to
rely on implicit casting. Just speculation: Perhaps add your own
specialization of BinOp(T, double) that does the right thing and is faster?
On Wed,
; Thanks,
>
> Toby
>
>
>
> *From: *Oleg Shirokobrod
> *Reply to: *"eigen@lists.tuxfamily.org"
> *Date: *Thursday, 4 June 2020 at 06:36
> *To: *"eigen@lists.tuxfamily.org"
> *Subject: *Re: [eigen] Using Eigen decompositions with ceres autodiff Jet
> d
. Should I create an issue on the
Eigen gitlab?
Thanks,
Toby
From: Oleg Shirokobrod
Reply to: "eigen@lists.tuxfamily.org"
Date: Thursday, 4 June 2020 at 06:36
To: "eigen@lists.tuxfamily.org"
Subject: Re: [eigen] Using Eigen decompositions with ceres autodiff Jet data
type.
1.
1. I would like to have autodiff ability, so I cannot use double for both A
and b. If I cast b: A.jacobiSVD().solve( b.cast()) everything works
fine, but BinOp(T,T) is more expensive than BinOp(T, double). I would like
to keep b as a vector of doubles.
2. T=Jet is ceres solver autodiff
Try to compile your code in debug mode with the type assertions on.
On Wed, Jun 3, 2020 at 1:14 PM Rasmus Munk Larsen
wrote:
> Are you saying that you compute the decomposition in one type and solve
> with a RHS of a different type? Why do you say that VS^-1U^T * b should be
> Matrix? That
Are you saying that you compute the decomposition in one type and solve
with a RHS of a different type? Why do you say that VS^-1U^T * b should be
Matrix? That makes an assumption about type coercion rules. In fact, you
cannot generally mix types in Eigen expressions without explicit casting,
and
Rasmuss, I do not quite understand this issue. Decomposition solve should
propagate scalar type of a matrix but not scalar type of its argument.
Example:
template Matrix A;
VectorXd b;
A.jcobiSVD().solve(b) should be of type Matrix but it is not. Type of
result is Matrix. If we make SVD
OK, I opened https://gitlab.com/libeigen/eigen/-/issues/1909 for this.
On Tue, Jun 2, 2020 at 11:06 PM Oleg Shirokobrod
wrote:
> Yes. At the time of computing only 1d observation (VectorXd) is known.
>
> On Tue, Jun 2, 2020 at 9:42 PM Rasmus Munk Larsen
> wrote:
>
>> OK, so b is declared as
Yes. At the time of computing only 1d observation (VectorXd) is known.
On Tue, Jun 2, 2020 at 9:42 PM Rasmus Munk Larsen
wrote:
> OK, so b is declared as VectorXf or some other type with
> ColsAtCompileTime=1?
>
> On Tue, Jun 2, 2020 at 11:27 AM Oleg Shirokobrod <
> oleg.shirokob...@gmail.com>
OK, so b is declared as VectorXf or some other type with
ColsAtCompileTime=1?
On Tue, Jun 2, 2020 at 11:27 AM Oleg Shirokobrod
wrote:
>
> Yes, b is measured spectrum that is 1d array. I have to get x for 1d array
> at a time. I fit sum of peak models to 1d rhs. 1d array of peak model
> values
Yes, b is measured spectrum that is 1d array. I have to get x for 1d array
at a time. I fit sum of peak models to 1d rhs. 1d array of peak model
values is one column of matrix A.
On Tue 2. Jun 2020 at 20.06, Rasmus Munk Larsen wrote:
> Why do you say that? You could be solving for multiple
Why do you say that? You could be solving for multiple right-hand sides. Is
b know to have 1 column at compile time?
On Tue, Jun 2, 2020 at 1:31 AM Oleg Shirokobrod
wrote:
> Hi Rasmus,
>
> I have just tested COD decomposition in Eigen library. It arises the same
> problem. This is defect of
Hi Rasmus,
I have just tested COD decomposition in Eigen library. It arises the same
problem. This is defect of Eigen decomposition module type reduction of
result of solve method. If
template Matrix A; and ArraXd b;, than
x = A.solve(b) should be of type Matrix.
I like the idea to use COD
Rasmus, I wiil have a look at COD. Brad, I did not try CppAD.I am working
in given framework: ceres nonlinear least squares solver + ceres autodiff +
Eigen decomposition modules SVD or QR. The problem is not just on autodiff
side. The problem is that Eigen decomposition modul does not work
On Mon, Jun 1, 2020 at 10:33 AM Patrik Huber wrote:
> Hi Rasmus,
>
> This is slightly off-topic to this thread here, but it would be great if
> you added your COD to the list/table of decompositions in Eigen:
> https://eigen.tuxfamily.org/dox/group__TopicLinearAlgebraDecompositions.html
>
>
Hi Patrik,
Thanks for mentioning. This is a known issue (see e.g.
https://gitlab.com/libeigen/eigen/-/issues/1889)
Cheers,
David
On 1. Jun 2020, 19:33 +0200, Patrik Huber , wrote:
> Hi Rasmus,
>
> This is slightly off-topic to this thread here, but it would be great if you
> added your COD to
Hi Rasmus,
This is slightly off-topic to this thread here, but it would be great if
you added your COD to the list/table of decompositions in Eigen:
https://eigen.tuxfamily.org/dox/group__TopicLinearAlgebraDecompositions.html
First, it would make it easier for people to find, and second, it
The matrix_solve_ls kernel in TensorFlow show a few different variants of
how to solve under- and over-determined least-squares problems using Either
LLT or COD:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/matrix_solve_ls_op_impl.h#L76
On Mon, Jun 1, 2020 at 9:58
Hi Oleg and Sameer,
A faster option than SVD, but more robust than QR (since it also handles
the under-determined case) is the complete orthogonal decomposition that I
implemented in Eigen a few years ago.
https://eigen.tuxfamily.org/dox/classEigen_1_1CompleteOrthogonalDecomposition.html
(Looks
Hi Sameer,
Thank you for quick reply and advices.
1. I implemented using both decompositions SVD and QR. QR decomposition has
the same problem as SVD for Jet but explicit expression for solution is
much more complex.
2. I implemented not only CostFunctor with operator()() for autodiff but
also
Oleg,
Two ideas:
1. You may have an easier time using QR factorization instead of SVD to
solve your least squares problem.
2. But you can do better, instead of trying to solve linear least squares
problem involving a matrix of Jets, you are better off, solving the linear
least squares problem on
Hi list, I am using Eigen 3.3.7 release with ceres solver 1.14.0 with
autodiff Jet data type and I have some problems. I need to solve linear
least square subproblem within variable projection algorithm, namely I need
to solve LLS equation
A(p)*x = b
Where matrix A(p) depends on nonlinear
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