What are global minima in optimization?

What are global minima in optimization?

Global minima refer to the lowest point in a function over its entire domain, where no other points have a lower value.

How do they differ from local minima?

Local minima are points that are lower than their immediate neighbors, but not necessarily the lowest overall. Global minima are the absolute lowest.

Why are global minima important in machine learning?

Finding global minima is crucial in machine learning as it ensures the best possible model performance by minimizing the loss function.

Can you give an example of finding a global minimum?

Sure! In a simple quadratic function like f(x) = (x-2)², the global minimum is at x=2, where the function value is 0.

What methods are used to find global minima?

Common methods include gradient descent, genetic algorithms, and simulated annealing, each with its own strengths.