SciPy - Advanced Optimization
When using basin-hopping to minimize a function with many local minima, which approach is most effective to improve the chance of finding the global minimum?
When using basin-hopping to minimize a function with many local minima, which approach is most effective to improve the chance of finding the global minimum?
hard📝 Application Q8 of 15
Step-by-Step Solution
Solution:
Step 1: Understand basin-hopping parameters
Step size controls the magnitude of random jumps; more iterations allow more exploration.Step 2: Effect of increasing step size and iterations
Larger steps help escape local minima; more iterations increase chances to find global minimum.Step 3: Evaluate other options
Reducing iterations or disabling local minimization reduces effectiveness; fixed seed affects reproducibility, not optimization quality.Final Answer:
Increase the step size of random perturbations and the number of iterations -> Option DQuick Check:
More iterations and bigger steps improve global search [OK]
Quick Trick: Bigger steps plus more iterations help escape local minima [OK]
Common Mistakes:
- Thinking fewer iterations improve results
- Disabling local minimization to speed up
- Confusing reproducibility with optimization quality
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