Timeline for algorithm for finding the minimizer of a almost convex function
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
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| Oct 11, 2016 at 3:35 | comment | added | Nawaf Bou-Rabee | Thanks for your clarification and for sharing this interesting paper, even though its assumptions may be tricky to verify in practice. | |
| Oct 11, 2016 at 0:55 | comment | added | Cristóbal Guzmán | Just to clarify: the method does not require arbitrary approximation to a Lipschitz convex function: $\epsilon$ is a parameter, which may be large or small. To my understanding, even deciding convexity is a hard problem, so there is no way to computationally verify almost-convexity either. The natural application of this setup (and the only one I am aware of) is a convex Lipschitz objective and optimization via evaluations with additive noise (so-called stochastic zero order convex optimization). | |
| Oct 9, 2016 at 17:44 | comment | added | Nawaf Bou-Rabee | How does one verify that a given function satisfies their hypotheses, i.e., can be approximated arbitrarily/uniformly well by a Lipschitz, convex function? | |
| Oct 9, 2016 at 14:48 | history | answered | Cristóbal Guzmán | CC BY-SA 3.0 |