Nonconvex Optimisation Using the PolyakŁojasiewicz Inequality
Add to your list(s)
Download to your calendar using vCal
If you have a question about this talk, please contact Mateja Jamnik.
We consider the idea of solving a nonconvex optimisation problem by adding a large enough stronglyconvex function to make the objective function convex. This allows the use of simpler convex optimisers, yet, given the large strongconvexity constant required, yields a value closer to the minimum of the function added than that of the objective one. We try to fix this with the novel idea of instead adding a function that makes the objective satisfy the PolyakŁojasiewicz (PL) inequality, a much weaker condition than strongconvexity. Building on previous work, we construct an optimisation algorithm relying on this method. We find that a much smaller multiplicative constant is needed for convergence to a minimum. We attempt to find and prove convergence rates and computational complexity and test which algorithm yields a more accurate minimum.
This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.
This talk is included in these lists:
Note that exdirectory lists are not shown.
