[Coin-ipopt] Derivative checker problem (IpOpt 3.3.4 over the Matlab interface), ignorance of starting point information
Liliya Kharevych
lilyukma at yahoo.com
Tue Jan 15 15:53:31 EST 2008
I think you can avoid this problem by setting:
app->Options()->SetNumericValue("point_perturbation_radius", 0.001); , or some other small number.
By default point perturbation radius is 10 and it seems like it is only used for derivative checker. That's why the initial guess for derivative checker is far off from the one you give.
I was using C++ though, so I am not sure if it is exactly the same problem for Matlab.
Lily
----- Original Message ----
From: Peter Carbonetto <pcarbo at cs.ubc.ca>
To: Sebastian Nowozin <nowozin at gmail.com>
Cc: coin-ipopt at list.coin-or.org
Sent: Monday, January 14, 2008 12:02:01 PM
Subject: Re: [Coin-ipopt] Derivative checker problem (IpOpt 3.3.4 over the Matlab interface), ignorance of starting point information
Sebastian,
I sent an email about this earlier:
http://list.coin-or.org/pipermail/coin-ipopt/2007-November/000914.html
You might try downloading version 3.3.1 of Ipopt; the derivative
checker seems to work in that release.
Are there any plans to fix this bug?
Peter Carbonetto
Ph.D. Candidate
Dept. of Computer Science
University of British Columbia
On Mon, 14 Jan 2008, Sebastian Nowozin wrote:
> Hello,
>
> it seems my previous mail was a little bit too fast. Infact I
believe
> there is a bug in either the Matlab interface or IpOpt derivative
> checker.
> I verified all my derivatives manually and by running
> finite-difference checks myself.
>
> The objective contains a sum of log(x) terms and I initialize x=1 for
> all these variables. The derivative checker documentation and code
> says the check is performed around the initial starting point.
> However, both the actual derivative value, as well as the
> finite-difference approximation are very large. I pinpointed the
> problem: the check is not performed around the given starting point,
> but around zero, x is tested at 1.0213e-08 and 2.1324e-10 instead of
1
> and 1 + 1e-8.
>
> This produces "errors" in the derivative check then, as a gradient of
> the logarithm at 2.1324e-10 is obviously difficult to approximate by
> finite differences.
>
> Starting derivative checker.
>
> * grad_f[ 0] = -1.1723767071873160e+08 ~
> -9.6725313515225500e+06 [ 1.112e+01]
> * grad_f[ 1] = -1.0738670595914723e+07 ~
> -4.1671285135755907e+06 [ 1.577e+00]
> * grad_f[ 2] = -5.8463631871292852e+06 ~
> -3.0138379141483540e+06 [ 9.398e-01]
> * grad_f[ 3] = -1.4526332092329852e+07 ~
> -4.7961759010235025e+06 [ 2.029e+00]
> * grad_f[ 4] = -8.6308330828022584e+06 ~
> -3.7335707020481834e+06 [ 1.312e+00]
> * grad_f[ 5] = -2.7622448487412033e+09 ~
> -1.7521032247221056e+07 [ 1.567e+02]
> * grad_f[ 6] = -2.6017683345673114e+07 ~
> -6.0855840781478323e+06 [ 3.275e+00]
> * grad_f[ 7] = -4.9277834346935088e+06 ~
> -2.7223418898174143e+06 [ 8.101e-01]
> ...
> * obj_hess[ 0, 0] = 5.4978685742214944e+17 v ~
> 1.1478986815970806e+16 [ 4.690e+01]
> * obj_hess[ 1, 1] = 4.6127618467025390e+15 v ~
> 8.7107723794524650e+14 [ 4.295e+00]
> * obj_hess[ 2, 2] = 1.3671985006328195e+15 v ~
> 4.0951923310177231e+14 [ 2.339e+00]
> * obj_hess[ 3, 3] = 8.4405729622660870e+15 v ~
> 1.2393410566196550e+15 [ 5.811e+00]
> * obj_hess[ 4, 4] = 2.9796511881277575e+15 v ~
> 6.6923364270269225e+14 [ 3.452e+00]
> * obj_hess[ 5, 5] = 3.0519986417589256e+20 v ~
> 2.7597471093476394e+17 [ 1.105e+03]
> * obj_hess[ 6, 6] = 2.7076793867028640e+16 v ~
> 2.3736845608057525e+15 [ 1.041e+01]
> * obj_hess[ 7, 7] = 9.7132198316959000e+14 v ~
> 3.2692188447254775e+14 [ 1.971e+00]
>
>
> I fear that also the optimization starts at this numerically
difficult
> points, using the "exact" Hessian. Can someone confirm this bug?
>
> Sebastian
>
> PS: I also send it to Peter Carbonetto, the author of the (excellent)
> Matlab interface.
>
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