What are local minima in optimization?
Local minima are points in a function where the value is lower than its neighboring points, but not necessarily the lowest overall.
Why is it important to overcome local minima?
Overcoming local minima is crucial because it allows us to find the global minimum, which leads to better solutions in optimization problems.
What techniques can I use to escape local minima?
Some techniques include using simulated annealing, genetic algorithms, or gradient descent with momentum.
Can you explain simulated annealing?
Simulated annealing is a probabilistic technique that explores the solution space by allowing occasional uphill moves to escape local minima.
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