Parameterized complexity

Parameterized complexity studies an algorithm's resource use as a function of both the encoded input length $N$ and a distinguished nonnegative integer parameter $k$. A small solution size, a dimension or a structural property of a graph can serve as the parameter. Fixed-parameter tractability allows arbitrary computable dependence on $k$ while keeping the exponent of $N$ independent of $k$.

A kernelization is a polynomial-time algorithm mapping a parameterized decision problem instance $(I,k)$ to an equivalent instance $(I^{\prime },k^{\prime })$ of the same problem, with both encoded length $|I^{\prime }|$ and parameter $k^{\prime }$ bounded by a computable function of $k$. Equivalent means that both instances have the same yes/no answer. The bound need not be a polynomial in $k$; a polynomial kernel is the stronger requirement that it is. For a decidable problem, exhaustively solving the bounded output leaves only a computable dependence on $k$, so kernelization yields fixed-parameter tractability.

A parameterized decision problem is fixed-parameter tractable if an algorithm decides every instance of encoded length $N$ and parameter $k$ in running time at most $f(k)N^c$, for a computable function $f$ and a constant $c$ independent of $k$. The distinction is in the exponent: $2^k{N}^3$ has this form, whereas an $N^k$ bound alone does not establish fixed-parameter tractability. Branching on an uncovered edge gives a fixed-parameter tractable vertex cover decision problem.