What the repository actually contains

Three CSV files under results/, holding 201 runs of a simulated annealer on a 0/1 knapsack. Every row records optimization_status. Every row says infeasible. Together they cost 8.94 hours of compute.

I published that. It sat there.

The three causes, all visible in the source

The temperature never means anything. The runs start at temperatures between 4×10⁵ and 1.02×10⁹, against an objective in the hundreds. Simulated annealing accepts an uphill move with probability exp(−Δ/T); when T is six orders of magnitude above Δ, that probability is indistinguishable from 1. Every uphill move is accepted, at every temperature, for the entire run. The cooling schedule is decoration. This is a random walk wearing an annealer's clothes.

The penalty pays you to overfill. An infeasible solution is charged a constant Big-M:

if backpack_capacity_respected(np_variable):
    return - backpack_value
else:
    return - backpack_value + BIG_M

Once you are over capacity, the charge stops growing — but −backpack_value keeps improving with every item you add. The search is being paid to overpack. Run it as published and the knapsack ends up loaded to roughly fifteen times its capacity.

There is no incumbent. get_solution() returns self.__solution, which is wherever the walk happened to stop. The class never keeps the best solution it has seen. Thousands of good states are visited and thrown away.

And a fourth thing, while we are here

__new_solution_is_better and __calculate_difference each call the objective twice, so every iteration evaluates it four times where two would do. The code also uses np.float and np.float128 — the first removed from NumPy, the second unavailable on ARM — so as committed it no longer runs at all.

Why this is on my portfolio

Because the fix is three lines and the diagnosis is the whole skill. Anyone can run a metaheuristic until it prints a number. Reading the acceptance rate, noticing it is pinned at 100%, and recognising that the temperature schedule was never in the loop — that is the part worth hiring.

The demo above runs the annealer in your browser on a fixed, seeded instance whose exact optimum a dynamic program computes in milliseconds, so every claim on the page is checkable against a proven number rather than against another heuristic.

The original generates a fresh random catalogue on every run with no seed, so the exact instances behind those 201 rows are unrecoverable. That is its own small lesson about publishing results.

Measure	Value
Committed runs	201
Feasible among them	0
Compute spent producing them	8.94 hours
Uphill moves accepted, as published	100.0%