Publication details

The Parameterized Complexity of Cascading Portfolio Scheduling



Year of publication 2019
Type Article in Proceedings
Conference Advances in Neural Information Processing Systems 32 (NIPS 2019)
MU Faculty or unit

Faculty of Informatics

Keywords Parameterized Complexity
Description Cascading portfolio scheduling is a static algorithm selection strategy which uses a sample of test instances to compute an optimal ordering (a cascading schedule) of a portfolio of available algorithms. The algorithms are then applied to each future instance according to this cascading schedule, until some algorithm in the schedule succeeds. Cascading algorithm scheduling has proven to be effective in several applications, including QBF solving and the generation of ImageNet classification models. It is known that the computation of an optimal cascading schedule in the offline phase is NP-hard. In this paper we study the parameterized complexity of this problem and establish its fixed-parameter tractability by utilizing structural properties of the success relation between algorithms and test instances. Our findings are significant as they reveal that in spite of the intractability of the problem in its general form, one can indeed exploit sparseness or density of the success relation to obtain non-trivial runtime guarantees for finding an optimal cascading schedule.

You are running an old browser version. We recommend updating your browser to its latest version.

More info