What are iterative algorithms in optimization?

What are iterative algorithms in optimization?

Iterative algorithms in optimization are methods that repeatedly refine solutions to find the best outcome. They use previous results to improve future iterations.

Can you give me an example of an iterative algorithm?

Sure! One common example is the Gradient Descent algorithm, which adjusts parameters to minimize a cost function by moving in the direction of the steepest descent.

How do these algorithms handle local minima?

Iterative algorithms can struggle with local minima. Techniques like random restarts or using momentum can help escape these traps and find better solutions.

What are the advantages of using iterative algorithms?

They are often more efficient for large problems, can handle complex landscapes, and allow for continuous improvement of solutions.

Are there any limitations to these algorithms?

Yes, they can be sensitive to initial conditions, may converge slowly, and sometimes require careful tuning of parameters.