What are the best practices in numerical differentiation?

What are the best practices in numerical differentiation?

When performing numerical differentiation, it's important to consider factors like step size, method choice, and error analysis.

Can you explain how step size affects the accuracy?

Certainly! A smaller step size generally increases accuracy but can also amplify numerical errors due to round-off.

What methods are commonly used for numerical differentiation?

Common methods include forward difference, backward difference, and central difference methods, each with its own pros and cons.

How do I choose the right method for my problem?

Choosing the right method depends on the function's behavior and the required accuracy. Central difference is often preferred for smooth functions.