Randomized algorithms
On fixed-parameter tractability and approximability of NP optimization problems
Journal of Computer and System Sciences - special issue on complexity theory
On Syntactic versus Computational Views of Approximability
SIAM Journal on Computing
Parameterizing above guaranteed values: MaxSat and MaxCut
Journal of Algorithms
New upper bounds for maximum satisfiability
Journal of Algorithms
On the advantage over a random assignment
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Parameterizing above or below guaranteed values
Journal of Computer and System Sciences
The complexity of finding subgraphs whose matching number equals the vertex cover number
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Exact algorithms for maximum acyclic subgraph on a superclass of cubic graphs
WALCOM'08 Proceedings of the 2nd international conference on Algorithms and computation
Systems of linear equations over F2 and problems parameterized above average
SWAT'10 Proceedings of the 12th Scandinavian conference on Algorithm Theory
Parameterized complexity of maxsat above average
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
Constraint satisfaction problems parameterized above or below tight bounds: a survey
The Multivariate Algorithmic Revolution and Beyond
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We show that every problem in MAX SNP has a lower bound on the optimum solution size that is unbounded and that the above guarantee question with respect to this lower bound is fixed parameter tractable. We next introduce the notion of “tight” upper and lower bounds for the optimum solution and show that the parameterized version of a variant of the above guarantee question with respect to the tight lower bound cannot be fixed parameter tractable unless P = NP, for a class of NP-optimization problems.