What are the limits of optimization algorithms?

What are the limits of optimization algorithms?

Optimization algorithms are powerful, but they have limitations such as local minima, computational complexity, and overfitting.

Can you explain what local minima means?

Local minima refer to points in the solution space where the algorithm finds a solution that is better than its neighbors, but not the best overall.

How do these limitations affect real-world applications?

In real-world applications, these limitations can lead to suboptimal solutions, increased computation time, and challenges in scalability.

Are there any techniques to overcome these limitations?

Yes, techniques like simulated annealing, genetic algorithms, and hybrid approaches can help navigate these limitations.