What are the limitations of gradient descent?

What are the limitations of gradient descent?

Gradient descent is a powerful optimization algorithm, but it has several limitations. For instance, it can get stuck in local minima, especially in non-convex functions. Additionally, the choice of learning rate is crucial; if it's too high, the algorithm may diverge, and if it's too low, convergence can be very slow.

Can you explain what local minima means?

Sure! Local minima refer to points in the optimization landscape where the function value is lower than its neighboring points, but not necessarily the lowest overall. This can be problematic for gradient descent, as it may stop at these points instead of finding the global minimum.

What are some alternatives to gradient descent?

Some alternatives include stochastic gradient descent (SGD), Adam optimizer, and Newton's method. Each has its own advantages and is suited for different types of problems.

How does the learning rate affect the performance of gradient descent?

The learning rate determines the size of the steps taken towards the minimum. A small learning rate can lead to slow convergence, while a large learning rate can cause overshooting and divergence. Finding the right balance is key to effective optimization.