NLP Scaling

obj_scaling_factor:

Scaling factor for the objective function.
This option sets a scaling factor for the objective function. The scaling is seen internally by Ipopt but the unscaled objective is reported in the console output. If additional scaling parameters are computed (e.g. user-scaling or gradient-based), both factors are multiplied. If this value is chosen to be negative, Ipopt will maximize the objective function instead of minimizing it. The valid range for this real option is ${\tt -inf} < {\tt obj\_scaling\_factor } < {\tt +inf}$ and its default value is $1$ .

nlp_scaling_method:

Select the technique used for scaling the NLP.
Selects the technique used for scaling the problem internally before it is solved. For user-scaling, the parameters come from the NLP. If you are using AMPL, they can be specified through suffixes ("scaling_factor") The default value for this string option is "gradient-based".
Possible values:

• none: no problem scaling will be performed
• user-scaling: scaling parameters will come from the user
• gradient-based: scale the problem so the maximum gradient at the starting point is scaling_max_gradient
• equilibration-based: scale the problem so that first derivatives are of order 1 at random points (only available with MC19)

nlp_scaling_max_gradient:

Maximum gradient after NLP scaling.
This is the gradient scaling cut-off. If the maximum gradient is above this value, then gradient based scaling will be performed. Scaling parameters are calculated to scale the maximum gradient back to this value. (This is g_max in Section 3.8 of the implementation paper.) Note: This option is only used if "nlp_scaling_method" is chosen as "gradient-based". The valid range for this real option is $0 < {\tt nlp\_scaling\_max\_gradient } < {\tt +inf}$ and its default value is $100$ .

nlp_scaling_min_value:

Minimum value of gradient-based scaling values.
This is the lower bound for the scaling factors computed by gradient-based scaling method. If some derivatives of some functions are huge, the scaling factors will otherwise become very small, and the (unscaled) final constraint violation, for example, might then be significant. Note: This option is only used if "nlp_scaling_method" is chosen as "gradient-based". The valid range for this real option is $0 \le {\tt nlp\_scaling\_min\_value } < {\tt +inf}$ and its default value is $1 \cdot 10^{-08}$ .