In general, Optimization Toolbox™ solvers iterate to find an optimum. This means a solver begins at an initial value x0, performs some intermediate calculations that eventually lead to a new point x1, and then repeats the process to find successive approximations x2, x3, ... of the local minimum. Processing stops after some number of iterations k.
At any step, intermediate calculations may involve evaluating the objective function and constraints, if any, at points near the current iterate xi. For example, the solver may estimate a gradient by finite differences. At each of these nearby points, the function count (F-count) is increased by one.
If there are no constraints, the F-count reports the total number of objective function evaluations.
If there are constraints, the F-count reports only the number of points where function evaluations took place, not the total number of evaluations of constraint functions.
If there are many constraints, the F-count can be significantly less than the total number of function evaluations.
F-count is a header in the iterative display for many solvers. For an example, see Interpreting the Result.
The F-count appears in the output structure as output.funcCount. This enables you to access the evaluation count programmatically. For more information on output structures, see Output Structures.
Sometimes a solver attempts a step, and rejects the attempt. The trust-region, trust-region-reflective, and trust-region-dogleg algorithms count these failed attempts as iterations, and report the (unchanged) result in the iterative display. The interior-point, active-set, and levenberg-marquardt algorithms do not count such an attempt as an iteration, and do not report the attempt in the iterative display. All attempted steps increase the F-count, regardless of the algorithm.