simanneal | Python module for Simulated Annealing optimization

Simulated Annealing TLDR: We're trying to find a set of parameters that will maximize a function by adding random noise to parameters. If change leads to improvement, changes are accepted; once in a while we accept negative changes, but the probability of that lowers with time and how bad the change is.

In the snippet above, the function actually uses multiple parameters but accepts them as a list:

• w is the function which parameters are optimized
• seed_x is the initial guess of parameters - can be selected at random, but an informed guess would be better
• Gaussian is just "shape" of the noise, such that small values are more common. random.random()*sigma (all values are equally likely) would work just fine there, too.
• sigma is the magnitude of noise to be injected. It should not exceed a couple percent of typical param values. If param values vastly differ in magnitude, consider using a list of sigmas specific for each parameter.
• MISSING: notion of temperature, which will actually make it simulated annealing

Rewriting it with temperature, more descriptive names, and more explicit: