Improved approximations for hard optimization problems via problem instance classification
Rainbow of computer science
The Multivariate Algorithmic Revolution and Beyond
Parameterized approximation via fidelity preserving transformations
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Hyper-T-width and hyper-D-width: Stable connectivity measures for hypergraphs
Theoretical Computer Science
Completely inapproximable monotone and antimonotone parameterized problems
Journal of Computer and System Sciences
An exponential time 2-approximation algorithm for bandwidth
Theoretical Computer Science
A novel parameterised approximation algorithm for minimum vertex cover
Theoretical Computer Science
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The notion of fixed-parameter approximation is introduced to investigate the approximability of optimization problems within the framework of fixed-parameter computation. This work partially aims at enhancing the world of fixed-parameter computation in parallel with the conventional theory of computation that includes both exact and approximate computations. In particular, it is proved that fixed-parameter approximability is closely related to the approximation of small-cost solutions in polynomial time. It is also demonstrated that many fixed-parameter intractable problems are not fixed-parameter approximable. On the other hand, fixed-parameter approximation appears to be a viable approach to solving some inapproximable yet important optimization problems. For instance, all problems in the class MAX SNP admit fixed-parameter approximation schemes in time O(2O((1−ε/O(1))k) p(n)) for any small ε0.