What are the different optimization techniques and how do they compare?

What are the different optimization techniques and how do they compare?

There are several optimization techniques, including gradient descent, genetic algorithms, and simulated annealing. Each has its strengths and weaknesses depending on the problem.

Can you explain how gradient descent works?

Gradient descent is an iterative optimization algorithm used to minimize a function by adjusting parameters in the opposite direction of the gradient.

What are the advantages of using genetic algorithms?

Genetic algorithms are great for solving complex problems where traditional methods fail. They explore a large search space and can find global optima.

How does simulated annealing differ from gradient descent?

Simulated annealing allows for occasional uphill moves to escape local minima, while gradient descent strictly moves downhill.

Which technique is best for large datasets?

For large datasets, gradient descent is often preferred due to its efficiency, especially with stochastic variations.