[Ipopt] large-scale quadratic optimization without constraints

[Tran Minh Tuan]

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,