Tuesday, 15:45 - 16:10 h, Room: H 2036


Olivier Devolder
Intermediate gradient methods for smooth convex optimization problems with inexact oracle

Coauthors: Fran├žois Glineur, Yurii Nesterov


Between the slow but robust gradient method and the fast but sensitive to errors fast gradient method, we develop new intermediate gradient methods for smooth convex optimization problems.
We show, theoretically and on numerical experiments, that these new intermediate first-order methods can be used in order to accelerate the minimization of a smooth convex function when only inexact first-order information is available.


Talk 2 of the invited session Tue.3.H 2036
"First-derivative and interior methods in convex optimization" [...]
Cluster 4
"Conic programming" [...]


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