Sentence examples for an optimization version from inspiring English sources

The MaxSAT problem is an optimization version of the well-known Boolean satisfiability problem (SAT) (Biere et al., 2009).

We show that finding a BTP is an NP-complete problem; derive an approximation algorithm for an optimization version of the problem; and present a recursive algorithm to find a BTP with errors in the input.

We are thus interested in an approximate, optimization version of the problem: A PPROXIMATE D IRECT I NTERACTION G RAPH F ROM C ONNECTIVITY M ATRIX (A-DIGCOM) Given: A connectivity matrix M n× n and a tolerance level 0 ≤ δ ≤ 1 Find: A graph G = (V, E) such that the number of pairs (u, v) ∈ V × V such that | P G u, v) - M u, v)| ≤ δ is maximized.

It is known that the problem is NP-hard and its optimization version admits a polynomial time approximation scheme (PTAS).

Maximum Satisfiability (MaxSAT) is the optimization version of the Satisfiability (SAT) problem.

The optimization version of the problem is to find the smallest ξ for which a routing of this kind exists.

Weighted Max-SAT is the optimization version of SAT and many important problems can be naturally encoded as such.

We develop exact formulations of the correlation clustering task as Maximum Satisfiability (MaxSAT), the optimization version of the Boolean satisfiability (SAT) problem.

Whereas many complexity results exist for the optimization version of the problem, complexity for the decision variant, which from a practical point of view is more important, is widely unknown.

Maximization (18) is the optimization version of the set packing problem, which is shown to be NP-hard [30].

For instance the optimization version of the problem \(\sc{VERTEX}\ \sc{COVER}\) defined below possesses a simple polynomial time approximation algorithm which allows us to find a solution (i.e. a set of vertices including at least one from each edge of the input graph) which is no larger than twice the size of an optimal solution.