NLP

bound_relax_factor:

Factor for initial relaxation of the bounds.
Before start of the optimization, the bounds given by the user are relaxed. This option sets the factor for this relaxation. If it is set to zero, then then bounds relaxation is disabled. (See Eqn.(35) in implementation paper.) The valid range for this real option is $0 \le {\tt bound\_relax\_factor } < {\tt +inf}$ and its default value is $1 \cdot 10^{-08}$ .

honor_original_bounds:

Indicates whether final points should be projected into original bounds.
Ipopt might relax the bounds during the optimization (see, e.g., option "bound_relax_factor"). This option determines whether the final point should be projected back into the user-provide original bounds after the optimization. The default value for this string option is "yes".
Possible values:

• no: Leave final point unchanged
• yes: Project final point back into original bounds

check_derivatives_for_naninf:

Indicates whether it is desired to check for Nan/Inf in derivative matrices
Activating this option will cause an error if an invalid number is detected in the constraint Jacobians or the Lagrangian Hessian. If this is not activated, the test is skipped, and the algorithm might proceed with invalid numbers and fail. If test is activated and an invalid number is detected, the matrix is written to output with print_level corresponding to J_MORE_DETAILED; so beware of large output! The default value for this string option is "no".
Possible values:

• no: Don't check (faster).
• yes: Check Jacobians and Hessian for Nan and Inf.

nlp_lower_bound_inf:

any bound less or equal this value will be considered -inf (i.e. not lower bounded).
The valid range for this real option is ${\tt -inf} < {\tt nlp\_lower\_bound\_inf } < {\tt +inf}$ and its default value is $-1 \cdot 10^{+19}$ .

nlp_upper_bound_inf:

any bound greater or this value will be considered +inf (i.e. not upper bounded).
The valid range for this real option is ${\tt -inf} < {\tt nlp\_upper\_bound\_inf } < {\tt +inf}$ and its default value is $1 \cdot 10^{+19}$ .

fixed_variable_treatment:

Determines how fixed variables should be handled.
The main difference between those options is that the starting point in the "make_constraint" case still has the fixed variables at their given values, whereas in the case "make_parameter" the functions are always evaluated with the fixed values for those variables. Also, for "relax_bounds", the fixing bound constraints are relaxed (according to" bound_relax_factor"). For both "make_constraints" and "relax_bounds", bound multipliers are computed for the fixed variables. The default value for this string option is "make_parameter".
Possible values:

• make_parameter: Remove fixed variable from optimization variables
• make_constraint: Add equality constraints fixing variables
• relax_bounds: Relax fixing bound constraints

jac_c_constant:

Indicates whether all equality constraints are linear
Activating this option will cause Ipopt to ask for the Jacobian of the equality constraints only once from the NLP and reuse this information later. The default value for this string option is "no".
Possible values:

• no: Don't assume that all equality constraints are linear
• yes: Assume that equality constraints Jacobian are constant

jac_d_constant:

Indicates whether all inequality constraints are linear
Activating this option will cause Ipopt to ask for the Jacobian of the inequality constraints only once from the NLP and reuse this information later. The default value for this string option is "no".
Possible values:

• no: Don't assume that all inequality constraints are linear
• yes: Assume that equality constraints Jacobian are constant

hessian_constant:

Indicates whether the problem is a quadratic problem
Activating this option will cause Ipopt to ask for the Hessian of the Lagrangian function only once from the NLP and reuse this information later. The default value for this string option is "no".
Possible values:

• no: Assume that Hessian changes
• yes: Assume that Hessian is constant