Cobb-douglas Functions Slows Running Tremendously. How To Expedite A Non-linear Calculation In Python?

I have a working microeconomic model running with 10 modules 2000 agents, for up to 10 years. The program was running fast, providing results, output and graphics in a matter of se

Solution 1:

It's hard to say how to fix it without seeing the context in the rest of your code; but one thing that might speed it up is pre-computing dummy quantities with numpy. For example, you could make a numpy array of each agent's total_balance and sum_qualification, compute a corresponding array of dummy_quantities and then assign that back to the agents.

Here's a highly-simplified demonstration of the speedup:

%%timeit
vals = range(100000)
new_vals = [v**0.5 for v in vals]
> 100 loops, best of 3: 15 ms per loop

Now, with numpy:

%%timeit
vals = np.array(range(100000))
new_vals = np.sqrt(vals)
>100 loops, best of3: 6.3 ms per loop

However, a slow-down from a few seconds to 3 minutes seems extreme for the difference in calculation. Is the model behaving the same way with the C-D function, or is that driving changes in the model dynamics which are the real reason for the slowdown? If the latter, then you might need to look elsewhere for the bottleneck to optimize.