What is the difference between the computation of gradient (the partial derivative of error w.r.t. weight) in gradient descent and the computation of the Jacobian in Levenberg-Marquardt algorithm?
2026-03-29 16:24:35.1774801475
Difference between gradient and Jacobian in gradient descent
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The "Jacobian" in this context is the Jacobian matrix of the gradient, which is the Hessian of the underlying objective function. It is therefore quite a bit more expensive to compute than the gradient (because you need $O(n^2)$ numerical second derivatives instead of $O(n)$ numerical first derivatives).