Subsections

Restoration Phase


expect_infeasible_problem:

Enable heuristics to quickly detect an infeasible problem.
This options is meant to activate heuristics that may speed up the infeasibility determination if you expect that there is a good chance for the problem to be infeasible. In the filter line search procedure, the restoration phase is called more quickly than usually, and more reduction in the constraint violation is enforced before the restoration phase is left. If the problem is square, this option is enabled automatically. The default value for this string option is "no".
Possible values:


expect_infeasible_problem_ctol:

Threshold for disabling "expect_infeasible_problem" option.
If the constraint violation becomes smaller than this threshold, the "expect_infeasible_problem" heuristics in the filter line search are disabled. If the problem is square, this options is set to 0. The valid range for this real option is $ 0 \le {\tt expect\_infeasible\_problem\_ctol } < {\tt +inf}$ and its default value is $ 0.001$.


expect_infeasible_problem_ytol:

Multiplier threshold for activating "expect_infeasible_problem" option.
If the max norm of the constraint multipliers becomes larger than this value and "expect_infeasible_problem" is chosen, then the restoration phase is entered. The valid range for this real option is $ 0 < {\tt expect\_infeasible\_problem\_ytol } < {\tt +inf}$ and its default value is $ 1 \cdot 10^{+08}$.


start_with_resto:

Tells algorithm to switch to restoration phase in first iteration.
Setting this option to "yes" forces the algorithm to switch to the feasibility restoration phase in the first iteration. If the initial point is feasible, the algorithm will abort with a failure. The default value for this string option is "no".
Possible values:


soft_resto_pderror_reduction_factor:

Required reduction in primal-dual error in the soft restoration phase.
The soft restoration phase attempts to reduce the primal-dual error with regular steps. If the damped primal-dual step (damped only to satisfy the fraction-to-the-boundary rule) is not decreasing the primal-dual error by at least this factor, then the regular restoration phase is called. Choosing "0" here disables the soft restoration phase. The valid range for this real option is $ 0 \le {\tt soft\_resto\_pderror\_reduction\_factor } < {\tt +inf}$ and its default value is $ 0.9999$.


required_infeasibility_reduction:

Required reduction of infeasibility before leaving restoration phase.
The restoration phase algorithm is performed, until a point is found that is acceptable to the filter and the infeasibility has been reduced by at least the fraction given by this option. The valid range for this real option is $ 0 \le {\tt required\_infeasibility\_reduction } < 1$ and its default value is $ 0.9$.


bound_mult_reset_threshold:

Threshold for resetting bound multipliers after the restoration phase.
After returning from the restoration phase, the bound multipliers are updated with a Newton step for complementarity. Here, the change in the primal variables during the entire restoration phase is taken to be the corresponding primal Newton step. However, if after the update the largest bound multiplier exceeds the threshold specified by this option, the multipliers are all reset to 1. The valid range for this real option is $ 0 \le {\tt bound\_mult\_reset\_threshold } < {\tt +inf}$ and its default value is $ 1000$.


constr_mult_reset_threshold:

Threshold for resetting equality and inequality multipliers after restoration phase.
After returning from the restoration phase, the constraint multipliers are recomputed by a least square estimate. This option triggers when those least-square estimates should be ignored. The valid range for this real option is $ 0 \le {\tt constr\_mult\_reset\_threshold } < {\tt +inf}$ and its default value is 0.


evaluate_orig_obj_at_resto_trial:

Determines if the original objective function should be evaluated at restoration phase trial points.
Setting this option to "yes" makes the restoration phase algorithm evaluate the objective function of the original problem at every trial point encountered during the restoration phase, even if this value is not required. In this way, it is guaranteed that the original objective function can be evaluated without error at all accepted iterates; otherwise the algorithm might fail at a point where the restoration phase accepts an iterate that is good for the restoration phase problem, but not the original problem. On the other hand, if the evaluation of the original objective is expensive, this might be costly. The default value for this string option is "yes".
Possible values: