15,583 research outputs found
On Complexity of Minimum Leaf Out-branching Problem
Given a digraph , the Minimum Leaf Out-Branching problem (MinLOB) is the
problem of finding in an out-branching with the minimum possible number of
leaves, i.e., vertices of out-degree 0. Gutin, Razgon and Kim (2008) proved
that MinLOB is polynomial time solvable for acyclic digraphs which are exactly
the digraphs of directed path-width (DAG-width, directed tree-width,
respectively) 0. We investigate how much one can extend this polynomiality
result. We prove that already for digraphs of directed path-width (directed
tree-width, DAG-width, respectively) 1, MinLOB is NP-hard. On the other hand,
we show that for digraphs of restricted directed tree-width (directed
path-width, DAG-width, respectively) and a fixed integer , the problem of
checking whether there is an out-branching with at most leaves is
polynomial time solvable
Clique-width: When Hard Does Not Mean Impossible
In recent years, the parameterized complexity approach has lead to the introduction of many new algorithms and frameworks on graphs and digraphs of bounded clique-width and, equivalently, rank-width. However, despite intensive work on the subject, there still exist well-established hard problems where neither a parameterized algorithm nor a theoretical obstacle to its existence are known. Our article is interested mainly in the digraph case, targeting the well-known Minimum Leaf Out-Branching (cf. also Minimum Leaf Spanning Tree) and Edge Disjoint Paths problems on digraphs of bounded clique-width with non-standard new approaches.
The first part of the article deals with the Minimum Leaf Out-Branching problem and introduces a novel XP-time algorithm wrt. clique-width. We remark that this problem is known to be W[2]-hard, and that our algorithm does not resemble any of the previously published attempts solving special cases of it such as the Hamiltonian Path. The second part then looks at the Edge Disjoint Paths problem (both on graphs and digraphs) from a different perspective -- rather surprisingly showing that this problem has a definition in the MSO_1 logic of graphs. The linear-time FPT algorithm wrt. clique-width then follows as a direct consequence
Parameterized Algorithms for Directed Maximum Leaf Problems
We prove that finding a rooted subtree with at least leaves in a digraph
is a fixed parameter tractable problem. A similar result holds for finding
rooted spanning trees with many leaves in digraphs from a wide family
that includes all strong and acyclic digraphs. This settles completely an open
question of Fellows and solves another one for digraphs in . Our
algorithms are based on the following combinatorial result which can be viewed
as a generalization of many results for a `spanning tree with many leaves' in
the undirected case, and which is interesting on its own: If a digraph of order with minimum in-degree at least 3 contains a rooted
spanning tree, then contains one with at least leaves
An FPT Algorithm for Directed Spanning k-Leaf
An out-branching of a directed graph is a rooted spanning tree with all arcs
directed outwards from the root. We consider the problem of deciding whether a
given directed graph D has an out-branching with at least k leaves (Directed
Spanning k-Leaf). We prove that this problem is fixed parameter tractable, when
k is chosen as the parameter. Previously this was only known for restricted
classes of directed graphs.
The main new ingredient in our approach is a lemma that shows that given a
locally optimal out-branching of a directed graph in which every arc is part of
at least one out-branching, either an out-branching with at least k leaves
exists, or a path decomposition with width O(k^3) can be found. This enables a
dynamic programming based algorithm of running time 2^{O(k^3 \log k)} n^{O(1)},
where n=|V(D)|.Comment: 17 pages, 8 figure
Space Saving by Dynamic Algebraization
Dynamic programming is widely used for exact computations based on tree
decompositions of graphs. However, the space complexity is usually exponential
in the treewidth. We study the problem of designing efficient dynamic
programming algorithm based on tree decompositions in polynomial space. We show
how to construct a tree decomposition and extend the algebraic techniques of
Lokshtanov and Nederlof such that the dynamic programming algorithm runs in
time , where is the maximum number of vertices in the union of
bags on the root to leaf paths on a given tree decomposition, which is a
parameter closely related to the tree-depth of a graph. We apply our algorithm
to the problem of counting perfect matchings on grids and show that it
outperforms other polynomial-space solutions. We also apply the algorithm to
other set covering and partitioning problems.Comment: 14 pages, 1 figur
Applications of incidence bounds in point covering problems
In the Line Cover problem a set of n points is given and the task is to cover
the points using either the minimum number of lines or at most k lines. In
Curve Cover, a generalization of Line Cover, the task is to cover the points
using curves with d degrees of freedom. Another generalization is the
Hyperplane Cover problem where points in d-dimensional space are to be covered
by hyperplanes. All these problems have kernels of polynomial size, where the
parameter is the minimum number of lines, curves, or hyperplanes needed. First
we give a non-parameterized algorithm for both problems in O*(2^n) (where the
O*(.) notation hides polynomial factors of n) time and polynomial space,
beating a previous exponential-space result. Combining this with incidence
bounds similar to the famous Szemeredi-Trotter bound, we present a Curve Cover
algorithm with running time O*((Ck/log k)^((d-1)k)), where C is some constant.
Our result improves the previous best times O*((k/1.35)^k) for Line Cover
(where d=2), O*(k^(dk)) for general Curve Cover, as well as a few other bounds
for covering points by parabolas or conics. We also present an algorithm for
Hyperplane Cover in R^3 with running time O*((Ck^2/log^(1/5) k)^k), improving
on the previous time of O*((k^2/1.3)^k).Comment: SoCG 201
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