# Documentation

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## Troubleshooting and Tips

Here is a list of typical problems and recommendations for dealing with them.

Problem

Recommendation

The solution found by `fminbnd` or `fminsearch` does not appear to be a global minimum.

There is no guarantee that you have a global minimum unless your problem is continuous and has only one minimum. Starting the optimization from a number of different starting points (or intervals in the case of `fminbnd`) may help to locate the global minimum or verify that there is only one minimum. Use different methods, where possible, to verify results.

Sometimes an optimization problem has values of `x` for which it is impossible to evaluate `f`.

Modify your function to include a penalty function to give a large positive value to `f` when infeasibility is encountered.

The minimization routine appears to enter an infinite loop or returns a solution that is not a minimum (or not a zero in the case of `fzero`).

Your objective function (`fun`) may be returning `NaN` or complex values. The optimization routines expect only real numbers to be returned. Any other values may cause unexpected results. To determine whether this is the case, set

```options = optimset('FunValCheck', 'on') ```

and call the optimization function with `options` as an input argument. This displays an error when the objective function returns `NaN` or complex values.

Optimization problems may take many iterations to converge. Most optimization problems benefit from good starting guesses. Providing good starting guesses improves the execution efficiency and may help locate the global minimum instead of a local minimum.

Sophisticated problems are best solved by an evolutionary approach, whereby a problem with a smaller number of independent variables is solved first. Solutions from lower order problems can generally be used as starting points for higher order problems by using an appropriate mapping.

The use of simpler cost functions and less stringent termination criteria in the early stages of an optimization problem can also reduce computation time. Such an approach often produces superior results by avoiding local minima.