I have a few optimization algorithms (for finding function minimum) and I'd like to check how good they are. Suppose I build test cases and compare the actual results to theoretical ones. What measures should I use to estimate if the function minimum was actually found?
I thought about:
- mean number of function evaluations (± standard deviation)
- success rate (how often it actually finds minimum)
Are there any others that I have missed (let's say algorithm finishes 1e-4 from known solution. so is it success already or not?)
My main concern is not time complexity. It is algorithm accuracy in cases when exact solution might never be found (eg. multidimensional solution spaces). How do I calculate the rate of convergence?