Lectures

  (Topics are tentative. Code is updated on t-square.) 8/21, 8/23. Gradient descent. Notes (draft) to minimize $latex f(x)$, take step along gradient of $latex f$. convex functions 8/28, 8/30. Faster Gradient descent, Newton method. Notes Take (near)-optimal step in gradient direction draft Newton-Raphson draft for root-finding:  $latex x^{t+1} = x^t - \frac{f(x)}{f'(x)}$ for optimization: $latex x^{t+1} =…