[Python] How do I interpret scipy.optimize.minimize convergence metrics? Specifically, nit,...

I am solving an constrained, nonlinear optimization problem with scipy.optimize.minimize using the SLSQP solver. The optimization converges properly, but I want to have a better understanding of how to interpret the OptimizeResult output, specifically nit, nfev, and njev. Scipy documentation defines them as the following:

• nit: Number of iterations performed by the optimizer.
• nfev, njev: Number of evaluations of the objective functions and of its Jacobian

And here is an example output from the optimizer which I would like to understand:

----------------------------------------
message: Optimization terminated successfully
success: True
status: 0
fun: -0.2498255944127068
x: [ 6.087e-02 7.000e-02 7.000e-02 7.000e-02 7.000e-02
7.000e-02 7.000e-02 7.000e-02]
nit: 6
jac: [-4.197e-02 -8.534e-02 -2.353e-02 -1.421e-02 -8.549e-02
-5.721e-02 -1.725e-02 -4.846e-03]
nfev: 54
njev: 6


My questions are:

• Why are there far more function evaluations than Jacobian evaluations? From my understanding, there should be a Jacobian evaluation with each update step.
• Do these convergence metrics give any indication of how stable this solution is, or how quickly it converged?
• In general, what can I learn about my optimization problem from these metrics alone, and what else may I have to dive deeper into in order to understand the objective landscape?

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