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# problem.nlpValidateKKT

Purpose
Validates the first order optimality conditions also known as the Karush-Kuhn-Tucker (KKT) conditions versus the currect solution
Topic areas
Solution, Nonlinear
Synopsis
`problem.nlpValidateKKT(mode, respectbasis, updatemult, violtarget)`
Arguments

mode | The calculation mode can be:
0 | recalculate the reduced costs at the current solution using the current dual solution.
1 | minimize the sum of KKT violations by adjusting the dual solution.
2 | perform both.
respectbasis | The following ways are defined to assess if a constraint is active:
0 | evaluate the recalculated slack activity versus SLPECFTOL_R.
1 | use the basis status of the slack in the linearized problem if available.
2 | use both.
updatemult | The calculated values can be:
0 | only used to calculate the NLPVALIDATIONINDEX_K measure.
1 | used to update the current dual solution and reduced costs.
violtarget | When calculating the best KKT multipliers, it is possible to enforce an even distribution of reduced costs violations by enforcing a bound on them.

Further information
The bounds enforced by violtarget are automatically relaxed if the desired accuracy cannot be achieved.
Parent Topic Methods of the class problem

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