Solution

ID: past-exam-of-the-mathematical-and-computational-biology-course-of-the-university-of-cambridge/2024/paper-2/a3/c/solution

Use reproducible random starts and retain the best successful result:
rng = np.random.default_rng(2024)
starts = rng.uniform(-4, 4, size=100)
results = [opt.minimize(lambda z: f(z[0]), x0=[x0]) for x0 in starts]
successful = [result for result in results if result.success]
best = min(successful, key=lambda result: result.fun)
print(best.x[0], best.fun)
The estimate is , . This random-restart optimization improves coverage of the different attraction regions, but their number alone does not guarantee global optimality.
Here an analytic check is possible. For , the envelope gives , above the candidate. On , . In the remaining interval , the derivative condition is equivalent, with , to . The left side decreases strictly from to while the right side increases, giving one minimum. This certifies the selected well as global rather than relying only on the restart experiment.

New to topics? Read the docs here!