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In this paper we introduce the convex fragment of Łukasiewicz Logic and discuss its possible applications in different learning schemes. Indeed, the provided theoretical results are highly general, because they can be exploited in any learning framework involving logical constraints. The method is of particular interest since the fragment guarantees to deal with convex constraints, which are shown to be equivalent to a set of linear constraints. Within this framework, we are able to formulatearXiv:1809.06778v1 fatcat:ioyw6wnd55eohfragg26w4azie