INFORMS Computing Society 

About: INFORMS Computing Society is an academic conference. The conference publishes majorly in the area(s): Asymptotic computational complexity & Dynamic priority scheduling. Over the lifetime, 4 publications have been published by the conference receiving 56 citations.

Fixed-Parameter Tractability and Parameterized Complexity Applied to Problems From Computational Social Choice

Abstract: This supplement provides a brief introduction to the field of fixed-parameter tractability and parameterized complexity. Some basic notions are explained and some related results are presented, with a focus on problems arising in the field of computational social choice. 1 Fixed-Parameter Tractability and Parameterized Complexity The study of fixed-parameter tractability and parameterized complexity has emerged as a new field within computational complexity theory in the late 1980s and the early 1990s. Since the early pioneering work of Downey, Fellows, and other researchers this area has established plenty of results, notions, and methods, and it provides a useful framework for dealing in practice with problems considered intractable by classical complexity theory. This supplement gives only a rough sketch of some basic notions and techniques within parameterized complexity theory; for a deeper and more comprehensive treatise, the textbooks by Downey and Fellows [DF99], Flum and Grohe [FG06], and Niedermeier [Nie06] and the survey by Buss and Islam [BI08] are highly recommendable. Let P and NP, respectively, denote the classical (worst-case) complexity classes deterministic polynomial time and nondeterministic polynomial time. Given any two decision problems, A and B, we say A polynomial-time many-one reduces to B (denoted A p m B) if there is a polynomial-time computable function f such that, for each input x, x A if and only if f x B. A set B is said to be NP-hard if for each NP set A, A p m B. If B NP is NP-hard then B is said to be NP-complete. Supported in part by the DFG under grants RO 1202/12-1 (within the European Science Foundation’s EUROCORES program LogICCC: “Computational Foundations of Social Choice”) and RO 1202/11-1 and by the Alexander von Humboldt Foundation’s TransCoop program.

Aeon: Synthesizing Scheduling Algorithms from High-Level Models

Abstract: This paper describes the aeon system whose aim is to synthesize scheduling algorithms from high-level models. Aeon, which is entirely written in comet, receives as input a high-level model for a scheduling application which is then analyzed to generate a dedicated scheduling algorithm exploiting the structure of the model. Aeon provides a variety of synthesizers for generating complete or heuristic algorithms. Moreover, synthesizers are compositional, making it possible to generate complex hybrid algorithms naturally. Preliminary experimental results indicate that this approach may be competitive with state-of-the-art search algorithms.

Energy-efficient task mapping for data-driven sensor network macroprogramming using constraint programming

Abstract: Energy-efficient task mapping for data-driven sensor network macroprogramming using constraint programming

Network Design with Compression: Complexity and Algorithms

Abstract: Recent advances in communication technology allow to compress data streams in communication networks by deploying physical devices (caches) at routers, yielding a more efficient usage of link capacities. This gives rise to the network design problem with compression (NDPC), a generalization of the classical Network Design problem. In this paper, we compare both problems, focusing on the computational complexity and analyzing the differences induced by the compression aspect. We show that the subproblem of adding compression, i.e., the compressor placement problem (CPP), is already weakly NP-hard, even on instances where Network Design alone is easy. We conclude with a pseudopolynomial algorithm for tree instances and a restricted polynomial case.