67 research outputs found
Solving Connectivity Problems Parameterized by Treedepth in Single-Exponential Time and Polynomial Space
A breakthrough result of Cygan et al. (FOCS 2011) showed that connectivity problems parameterized by treewidth can be solved much faster than the previously best known time ?^*(2^{?(twlog tw)}). Using their inspired Cut&Count technique, they obtained ?^*(?^tw) time algorithms for many such problems. Moreover, they proved these running times to be optimal assuming the Strong Exponential-Time Hypothesis. Unfortunately, like other dynamic programming algorithms on tree decompositions, these algorithms also require exponential space, and this is widely believed to be unavoidable. In contrast, for the slightly larger parameter called treedepth, there are already several examples of matching the time bounds obtained for treewidth, but using only polynomial space. Nevertheless, this has remained open for connectivity problems.
In the present work, we close this knowledge gap by applying the Cut&Count technique to graphs of small treedepth. While the general idea is unchanged, we have to design novel procedures for counting consistently cut solution candidates using only polynomial space. Concretely, we obtain time ?^*(3^d) and polynomial space for Connected Vertex Cover, Feedback Vertex Set, and Steiner Tree on graphs of treedepth d. Similarly, we obtain time ?^*(4^d) and polynomial space for Connected Dominating Set and Connected Odd Cycle Transversal
ETH Tight Algorithms for Geometric Intersection Graphs: Now in Polynomial Space
De Berg et al. in [SICOMP 2020] gave an algorithmic framework for subexponential algorithms on geometric graphs with tight (up to ETH) running times. This framework is based on dynamic programming on graphs of weighted treewidth resulting in algorithms that use super-polynomial space. We introduce the notion of weighted treedepth and use it to refine the framework of de Berg et al. for obtaining polynomial space (with tight running times) on geometric graphs. As a result, we prove that for any fixed dimension d ≥ 2 on intersection graphs of similarly-sized fat objects many well-known graph problems including Independent Set, r-Dominating Set for constant r, Cycle Cover, Hamiltonian Cycle, Hamiltonian Path, Steiner Tree, Connected Vertex Cover, Feedback Vertex Set, and (Connected) Odd Cycle Transversal are solvable in time 2^(n^{1-1/d}) and within polynomial space.publishedVersio
Hamiltonian Cycle Parameterized by Treedepth in Single Exponential Time and Polynomial Space
For many algorithmic problems on graphs of treewidth , a standard dynamic
programming approach gives an algorithm with time and space complexity
. It turns out that when one
considers the more restrictive parameter treedepth, it is often the case that a
variation of this technique can be used to reduce the space complexity to
polynomial, while retaining time complexity of the form
, where is the treedepth. This
transfer of methodology is, however, far from automatic. For instance, for
problems with connectivity constraints, standard dynamic programming techniques
give algorithms with time and space complexity on graphs of treewidth , but it is not clear how to
convert them into time-efficient polynomial space algorithms for graphs of low
treedepth.
Cygan et al. (FOCS'11) introduced the Cut&Count technique and showed that a
certain class of problems with connectivity constraints can be solved in time
and space complexity . Recently,
Hegerfeld and Kratsch (STACS'20) showed that, for some of those problems, the
Cut&Count technique can be also applied in the setting of treedepth, and it
gives algorithms with running time
and polynomial space usage. However, a number of important problems eluded such
a treatment, with the most prominent examples being Hamiltonian Cycle and
Longest Path.
In this paper we clarify the situation by showing that Hamiltonian Cycle,
Hamiltonian Path, Long Cycle, Long Path, and Min Cycle Cover all admit
-time and polynomial space algorithms on graphs of
treedepth . The algorithms are randomized Monte Carlo with only false
negatives.Comment: Presented at WG2020. 20 pages, 2 figure
Isolation Schemes for Problems on Decomposable Graphs
The Isolation Lemma of Mulmuley, Vazirani and Vazirani [Combinatorica'87] provides a self-reduction scheme that allows one to assume that a given instance of a problem has a unique solution, provided a solution exists at all. Since its introduction, much effort has been dedicated towards derandomization of the Isolation Lemma for specific classes of problems. So far, the focus was mainly on problems solvable in polynomial time. In this paper, we study a setting that is more typical for -complete problems, and obtain partial derandomizations in the form of significantly decreasing the number of required random bits. In particular, motivated by the advances in parameterized algorithms, we focus on problems on decomposable graphs. For example, for the problem of detecting a Hamiltonian cycle, we build upon the rank-based approach from [Bodlaender et al., Inf. Comput.'15] and design isolation schemes that use - random bits on graphs of treewidth at most ; - random bits on planar or -minor free graphs; and - -random bits on general graphs. In all these schemes, the weights are bounded exponentially in the number of random bits used. As a corollary, for every fixed we obtain an algorithm for detecting a Hamiltonian cycle in an -minor-free graph that runs in deterministic time and uses polynomial space; this is the first algorithm to achieve such complexity guarantees. For problems of more local nature, such as finding an independent set of maximum size, we obtain isolation schemes on graphs of treedepth at most that use random bits and assign polynomially-bounded weights. We also complement our findings with several unconditional and conditional lower bounds, which show that many of the results cannot be significantly improved
Space-Efficient Parameterized Algorithms on Graphs of Low Shrubdepth
Dynamic programming on various graph decompositions is one of the most
fundamental techniques used in parameterized complexity. Unfortunately, even if
we consider concepts as simple as path or tree decompositions, such dynamic
programming uses space that is exponential in the decomposition's width, and
there are good reasons to believe that this is necessary. However, it has been
shown that in graphs of low treedepth it is possible to design algorithms which
achieve polynomial space complexity without requiring worse time complexity
than their counterparts working on tree decompositions of bounded width. Here,
treedepth is a graph parameter that, intuitively speaking, takes into account
both the depth and the width of a tree decomposition of the graph, rather than
the width alone.
Motivated by the above, we consider graphs that admit clique expressions with
bounded depth and label count, or equivalently, graphs of low shrubdepth (sd).
Here, sd is a bounded-depth analogue of cliquewidth, in the same way as td is a
bounded-depth analogue of treewidth. We show that also in this setting,
bounding the depth of the decomposition is a deciding factor for improving the
space complexity. Precisely, we prove that on -vertex graphs equipped with a
tree-model (a decomposition notion underlying sd) of depth and using
labels, we can solve
- Independent Set in time using
space;
- Max Cut in time using space; and
- Dominating Set in time using space via
a randomized algorithm.
We also establish a lower bound, conditional on a certain assumption about
the complexity of Longest Common Subsequence, which shows that at least in the
case of IS the exponent of the parametric factor in the time complexity has to
grow with if one wishes to keep the space complexity polynomial.Comment: Conference version to appear at the European Symposium on Algorithms
(ESA 2023
On space efficiency of algorithms working on structural decompositions of graphs
Dynamic programming on path and tree decompositions of graphs is a technique
that is ubiquitous in the field of parameterized and exponential-time
algorithms. However, one of its drawbacks is that the space usage is
exponential in the decomposition's width. Following the work of Allender et al.
[Theory of Computing, '14], we investigate whether this space complexity
explosion is unavoidable. Using the idea of reparameterization of Cai and
Juedes [J. Comput. Syst. Sci., '03], we prove that the question is closely
related to a conjecture that the Longest Common Subsequence problem
parameterized by the number of input strings does not admit an algorithm that
simultaneously uses XP time and FPT space. Moreover, we complete the complexity
landscape sketched for pathwidth and treewidth by Allender et al. by
considering the parameter tree-depth. We prove that computations on tree-depth
decompositions correspond to a model of non-deterministic machines that work in
polynomial time and logarithmic space, with access to an auxiliary stack of
maximum height equal to the decomposition's depth. Together with the results of
Allender et al., this describes a hierarchy of complexity classes for
polynomial-time non-deterministic machines with different restrictions on the
access to working space, which mirrors the classic relations between treewidth,
pathwidth, and tree-depth.Comment: An extended abstract appeared in the proceedings of STACS'16. The new
version is augmented with a space-efficient algorithm for Dominating Set
using the Chinese remainder theore
Subexponential parameterized algorithms for graphs of polynomial growth
We show that for a number of parameterized problems for which only time algorithms are known on general graphs, subexponential
parameterized algorithms with running time are possible for graphs of polynomial growth with growth
rate (degree) , that is, if we assume that every ball of radius
contains only vertices. The algorithms use the technique of
low-treewidth pattern covering, introduced by Fomin et al. [FOCS 2016] for
planar graphs; here we show how this strategy can be made to work for graphs
with polynomial growth.
Formally, we prove that, given a graph of polynomial growth with growth
rate and an integer , one can in randomized polynomial time find a
subset such that on one hand the treewidth of is
, and on the other hand for every set of size at most , the probability that is
. Together with standard dynamic
programming techniques on graphs of bounded treewidth, this statement gives
subexponential parameterized algorithms for a number of subgraph search
problems, such as Long Path or Steiner Tree, in graphs of polynomial growth.
We complement the algorithm with an almost tight lower bound for Long Path:
unless the Exponential Time Hypothesis fails, no parameterized algorithm with
running time is possible for
any and an integer
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