Keyword Analysis & Research: newton's method vs gradient descent


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Frequently Asked Questions

Is Newton's method faster than gradient descent?

Where applicable, Newton's method converges much faster towards a local maximum or minimum than gradient descent.

What happens if gradient descent encounters a stationary point during iteration?

If gradient descent encounters a stationary point during iteration, the program continues to run, albeit the parameters don’t update. Newton’s method, however, requires to compute for . The program that runs it would therefore terminate with a division by zero error.

How do you do gradient descent with a function?

In gradient descent we only use the gradient (first order). In other words, we assume that the function ℓ around w is linear and behaves like ℓ ( w) + g ( w) ⊤ s. Our goal is to find a vector s that minimizes this function.

What is the difference between steepest descent and gradient descent?

In gradient descent we only use the gradient (first order). In other words, we assume that the function ℓ around w is linear and behaves like ℓ ( w) + g ( w) ⊤ s. Our goal is to find a vector s that minimizes this function. In steepest descent we simply set for some small α >0.


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