Can you explain what local minima are in optimization?
Sure! Local minima are points in a function where the value is lower than at nearby points, but not necessarily the lowest overall.
How do local minima affect optimization algorithms?
They can cause algorithms to get stuck, as they might stop searching for a better solution once they reach a local minimum.
Are there methods to avoid getting stuck in local minima?
Yes, techniques like simulated annealing, genetic algorithms, and using momentum can help escape local minima.
Can you give an example of a function with local minima?
Certainly! The function f(x) = x^4 - 3x^3 + 2 has local minima at certain points, which can be found using calculus.
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