AbstractFixed-parameter tractability of NP optimization problems is studied by relating it to approximability of the problems. It is shown that an NP optimization problem is fixed-parameter tractable if it admits a fully polynomial-time approximation scheme, or if it belongs to the class MAX SNP or to the class MIN F+Π1. This provides strong evidence that noW[1]-hard NP optimization problems belong to these optimization classes and includes a very large class of approximable optimization problems into the class of fixed-parameter tractable problems. Evidence is also demonstrated to support the current working hypothesis in the theory of fixed-parameter tractability
According to the theory of NPcompleteness, many problems that have important realworld applications ...
AbstractMany relevant intractable problems become tractable if some problem parameter is fixed. Howe...
When dealing with hard computational problems (NP-complete or worse), Scientists, Engineers and othe...
AbstractFixed-parameter tractability of NP optimization problems is studied by relating it to approx...
AbstractIn this paper, we study the relationship between the approximability and the parameterized c...
AbstractWe consider new parameterizations of NP-optimization problems that have nontrivial lower and...
AbstractWe study a refined framework of parameterized complexity theory where the parameter dependen...
AbstractWe define a natural variant of NP, MAX NP, and also a subclass called MAX SNP. These are cla...
In this note, we show, through the use of examples, how generic results for proving fixed-parameter ...
AbstractFixed-parameter intractability of optimization problems in NP is studied based on computatio...
The relation of constant-factor approximability to fixed-parameter tractability and kernelization is...
AbstractFor many fixed-parameter problems that are trivially solvable in polynomial-time, such as k-...
AbstractMany natural computational problems have input consisting of two or more parts, one of which...
AbstractWe introduce a formal framework for studying approximation properties of NP optimization (NP...
In this talk we discuss briefly classes of fixed-parameter tractability as well as approximation alg...
According to the theory of NPcompleteness, many problems that have important realworld applications ...
AbstractMany relevant intractable problems become tractable if some problem parameter is fixed. Howe...
When dealing with hard computational problems (NP-complete or worse), Scientists, Engineers and othe...
AbstractFixed-parameter tractability of NP optimization problems is studied by relating it to approx...
AbstractIn this paper, we study the relationship between the approximability and the parameterized c...
AbstractWe consider new parameterizations of NP-optimization problems that have nontrivial lower and...
AbstractWe study a refined framework of parameterized complexity theory where the parameter dependen...
AbstractWe define a natural variant of NP, MAX NP, and also a subclass called MAX SNP. These are cla...
In this note, we show, through the use of examples, how generic results for proving fixed-parameter ...
AbstractFixed-parameter intractability of optimization problems in NP is studied based on computatio...
The relation of constant-factor approximability to fixed-parameter tractability and kernelization is...
AbstractFor many fixed-parameter problems that are trivially solvable in polynomial-time, such as k-...
AbstractMany natural computational problems have input consisting of two or more parts, one of which...
AbstractWe introduce a formal framework for studying approximation properties of NP optimization (NP...
In this talk we discuss briefly classes of fixed-parameter tractability as well as approximation alg...
According to the theory of NPcompleteness, many problems that have important realworld applications ...
AbstractMany relevant intractable problems become tractable if some problem parameter is fixed. Howe...
When dealing with hard computational problems (NP-complete or worse), Scientists, Engineers and othe...