Hi, Could I ask for your collective advice? While writing examples for students I found an instance of `fmin_powell` claiming success while returning an incorrect minimum, on a very simple least-squares problem. I've put up the reproducer in this repository: https://github.com/matthew-brett/powell-fails The take-home is that, for a simple least-squares problem, and ordinary-looking data, for a particular starting value, `fmin_powell` stops on a not-minimum value and claims success, where other optimizers do find the minimum, as does the Octave implementation. Here is the output from the reproducer: LS inter: 2.114798570871842 LS slope: 0.5088641205763367 LS SSE error: 1.0674470960898728 BGFS minimization: Optimization terminated successfully. Current function value: 1.067447 Iterations: 3 Function evaluations: 15 Gradient evaluations: 5 [2.11480099 0.50886336] Powell minimization: Optimization terminated successfully. Current function value: 1.074363 Iterations: 6 Function evaluations: 152 [2.22069108 0.47457665] As you can see, the Powell result has a higher sum of squared error, different parameters, and claims success. I get this for Scipy 1.10.1 on Mac M2, Intel, and Ubuntu 22.04 amd64. I notice that we have a "modified" Powell implementation. Can I ask for hints as to where to go next in exploring this problem? Could there be a flaw in our implementation? Cheers, Matthew