What are global minima solutions in optimization?

What are global minima solutions in optimization?

Global minima solutions refer to the lowest point in a function across its entire domain, where the function achieves its minimum value.

How do they differ from local minima?

Local minima are points where the function value is lower than its neighboring points, but not necessarily the lowest overall.

Why are global minima important in machine learning?

Global minima are crucial because they represent the best possible solution for a model, leading to optimal performance.

Can you give an example of finding a global minimum?

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

What methods are used to find global minima?

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