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Lecture 4 | September 11 4.1 Gradient Descent
users.ece.utexas.eduEE 381V Lecture 4 | September 11 Fall 2012 4.1.1 Strong Convexity and implications De nition: If there exist a constant m>0 such that r2f mIfor all x2S, then the function f(x) is a strongly convex function on S.
6.1 Gradient Descent: Convergence Analysis
www.stat.cmu.eduLecture 6: September 12 6-3 6.1.2 Convergence of gradient descent with adaptive step size We will not prove the analogous result for gradient descent with backtracking to adaptively select the step size. Instead, we just present the result with a few comments.