Parameterized learning complexity
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Fixed-parameter tractability and completeness II: on completeness for W[1]
Theoretical Computer Science
The parameterized complexity of sequence alignment and consensus
Theoretical Computer Science
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Parameterized Complexity of Some Problems in Logic and Linguistics
LFCS '94 Proceedings of the Third International Symposium on Logical Foundations of Computer Science
Fixed-Parameter Complexity and Cryptography
AAECC-10 Proceedings of the 10th International Symposium on Applied Algebra, Algebraic Algorithms and Error-Correcting Codes
Fixed-Parameter Intractability II (Extended Abstract)
STACS '93 Proceedings of the 10th Annual Symposium on Theoretical Aspects of Computer Science
On the Structure of Parameterized Problems in NP (Extended Abstract)
STACS '94 Proceedings of the 11th Annual Symposium on Theoretical Aspects of Computer Science
The Parameterized Complexity of Sequence Alignment and Consensus
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
DNA Physical Mapping: Three Ways Difficult
ESA '93 Proceedings of the First Annual European Symposium on Algorithms
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
On the complexity of global scheduling constraints under structural restrictions
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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It is shown that the Precedence Constrained K-Processor Scheduling problem is hard for the parameterized complexity class W[2]. This means that there does not exist a constant c, such that for all fixed K, the Precedence Constrained K-Processor Scheduling problem can be solved in O(n^c) time, unless an unlikely collapse occurs in the parameterized complexity hierarchy. That is, if the problem can be solved in polynomial time for each fixed K, then it is likely that the degree of the running time polynomial must increase as the number of processors K increases.