[Ipopt] run time constraint violation

[Hans Pirnay]

Ipopt does not guarantee the equality constraints to hold at all
times. However, the bounds you specified on the variables will always
hold. Therefore, if you have a term like

h(x) = sqrt(f(x))

try introducing a variable y with

y = f(x)
h(x) = sqrt(y)
y>=0

or better yet, get rid of the sqrt altogether

y = f(x)
z*z = y
h(x) = z
y >=0
z>=0

However, note that any formulation involving bilinear terms ( x * y )
must not be evaluated at (x=0, y=0), because of the derivatives. So
you will need to specify starting values for y and z > epsilon

hope this helps

On Wed, Apr 4, 2012 at 5:34 AM, Ming Ji <finalguy at gmail.com> wrote:
> --> Starting line search in iteration 2 <--
> Mu has changed in line search - resetting watchdog counters.
> Acceptable Check:
>   overall_error =  3.3081951656527707e+01   acceptable_tol_             =
>  9.9999999999999995e-07
>   dual_inf      =  1.0184827638240758e+03   acceptable_dual_inf_tol_    =
>  1.0000000000000000e+10
>   constr_viol   =  0.0000000000000000e+00   acceptable_constr_viol_tol_ =
>  1.0000000000000000e-08
>   compl_inf     =  4.5618525445151024e+00   acceptable_compl_inf_tol_   =
>  1.0000000000000000e-02
>   curr_obj_val_ =  2.7727963512437492e-01   last_obj_val                =
>  1.3239460189118296e-01
>   fabs(curr_obj_val_-last_obj_val_)/Max(1., fabs(curr_obj_val_)) =
>  1.4488503323319196e-01 acceptable_obj_change_tol_ =  1.0000000000000000e+20
> test iter = 2
> The current filter has 0 entries.
> Relative step size for delta_x = 1.790897e-01
> minimal step size ALPHA_MIN = 6.253269E-14
> Starting checks for alpha (primal) = 1.00e+00
>
>
> On Tue, Apr 3, 2012 at 9:02 PM, Ming Ji <finalguy at gmail.com> wrote:
>>
>> I am trying to solve a NLP problem with IPOPT. I noticed that constraints
>> can be violated during the optimization. I need to take sqrt to estimate one
>> term in my target function. So it is natural to add a constraint to make
>> sure the sqrt calculation is always valid. However, during the iterations,
>> IPOPT use some trial parameters that violate this constraint. The
>> optimization almost always terminates before it finds any solution. Any work
>> around for this?
>
>
>
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