The SQP solver has found a local minimum within the convergence tolerance specified.

The SQP solver has reached the limit on the maximum number of iterations, but has not satisfied the convergence criteria.

The SQP solver has spent more time than the prespecified maximum real time for the optimization process, but has not satisfied the convergence criteria.

The SQP solver cannot make any progress in the line search. One possibility is that a solution has been found, but the convergence tolerance is set too small.

The NLP problem is too large for the SQP solver to solve. This could be due to a limitation in the memory allocated to the procedure.

The objective function cannot be evaluated at the starting point.

At least one of the constraints cannot be evaluated at the starting point.

The gradient of the objective function cannot be evaluated at the starting point.

The gradient of at least one of the constraints cannot be evaluated at the starting point.

The NLP problem has not been solved. There could be several reasons why this happens. For instance, maybe a QP subproblem could not be solved within a preset number of iterations.

SQP has located a point satisfying the first-order condition but not the second-order condition.

SQP has located a feasible point, but its objective function value is extremely low. SQP can also improve the objective function even further.

SQP cannot locate a feasible point.

SQP has identified an infeasibility in the problem.

The problem was solved by the OPTMODEL presolver.