Generalized Low Rank Models — mentioned by LolWolf

Mon, 12 Nov 2018 23:37:37 GMT

1. > It's theoretically impossible to guarantee a convergence to global optima using gradient descent if the function is non-convex. This is false. See, e.g., [0][1]. 2. I'm not really sure what the question is here. 3. If your loss is bounded from below (it is a square norm) by 0 and you achieve 0 loss, this means that 0 is a global optimum, since, by definition, no other objective value can be smaller than this number. --- [0] Theorem A.2 in Udell's Generalized Low-Rank models paper https://arxiv.org/pdf/1410.0342.pdf [1] B&V Convex Optimization (https://web.stanford.edu/~boyd/cvxbook/)…

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Generalized Low Rank Models · HackerPapers discussions

Generalized Low Rank Models — mentioned by LolWolf