What are local minima issues in optimization?

What are local minima issues in optimization?

Local minima issues occur when an optimization algorithm finds a solution that is better than its neighbors but not the best overall.

How do local minima affect machine learning models?

They can lead to suboptimal performance, as the model may settle for a solution that isn't the best fit for the data.

What techniques can help avoid local minima?

Techniques like using momentum, simulated annealing, or employing different initialization strategies can help.

Can you give an example of a local minima problem?

Sure! In training neural networks, if the loss function has multiple valleys, the optimizer might get stuck in a local valley.

Are there any algorithms specifically designed to handle local minima?

Yes, algorithms like genetic algorithms and particle swarm optimization are designed to explore the solution space more broadly.