[Ipopt] large-scale quadratic optimization without constraints

[Andreas Waechter]

Hi,

If you don't provide the Hessian for a nonlinear problem, you will have to 
choose the quasi-Newton approximation option

hessian_approximation limited-memory

Maybe you are already doing this...?  What options are you setting?

You say that you are solving a QP, so I assume that you already have the 
Hessian matrix somewhere.  Why do you not provide it to Ipopt? 
Convergence would probably quite a bit faster, unless the Hessian is 
dense.

The error you describe looks like there is some wrong information provided 
to Ipopt (e.g., if NaN ends up in Hessian entries and you didn't choose a 
Hessian approximation).  Did you run your code through a memory checker 
(like valgrind)?

Andreas

On Tue, 24 Mar 2009, Tran Minh Tuan wrote:

> Hi all,
>
> I am using Ipopt to solve a quadratic optimization problem without
> constraints (but only bound constraints on variables).
> In this case, the constraint number is set to zero, the gradient of
> the objective function is computed but the hessain is not.
> So the result is like that all the time:
>
> ====
> iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du
> alpha_pr  ls
>    0  2.0416444e+01 0.00e+00 5.95e+00   0.0 0.00e+00    -  0.00e+00
> 0.00e+00   0
>    1  1.6443076e+01 0.00e+00 1.21e+01  -6.2 5.95e+00  -4.0 1.00e+00
> 4.06e-01f  1
> ERROR: Problem in step computation, but emergency mode cannot be
> activated.
>
> .....
> Number of inequality constraint Jacobian evaluations = 0
> Number of Lagrangian Hessian evaluations             = 0
> Total CPU secs in IPOPT (w/o function evaluations)   =      0.009
> Total CPU secs in NLP function evaluations           =      0.000
>
> EXIT: Error in step computation (regularization becomes too large?)!
>
>
> Objective value
> f(x*) = 1.644308e+01
> ====
>
> I am wondering that in this kind of optimization, we MUST provide the
> hessain matrix ? ou there is something wrong somewhere ?
>
> Your experience would help me much,
> Thanks,
>
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