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Register a new userClassification of distributed binary labeling problems
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Jukka Suomela
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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We present a complete classification of the deterministic distributed time complexity for a family of graph problems: binary labeling problems in trees. These are locally checkable problems that can be encoded with an alphabet of size two in the edge labeling formalism. Examples of binary labeling problems include sinkless orientation, sinkless and sourceless orientation, 2-vertex coloring, perfect matching, and the task of coloring edges red and blue such that all nodes are incident to at least one red and at least one blue edge. More generally, we can encode e.g. any cardinality constraints on indegrees and outdegrees. We study the deterministic time complexity of solving a given binary labeling problem in trees, in the usual LOCAL model of distributed computing. We show that the complexity of any such problem is in one of the following classes: $O(1)$, $Theta(log n)$, $Theta(n)$, or unsolvable. In particular, a problem that can be represented in the binary labeling formalism cannot have time complexity $Theta(log^* n)$, and hence we know that e.g. any encoding of maximal matchings has to use at least three labels (which is tight). Furthermore, given the description of any binary labeling problem, we can easily determine in which of the four classes it is and what is an asymptotically optimal algorithm for solving it. Hence the distributed time complexity of binary labeling problems is decidable, not only in principle, but also in practice: there is a simple and efficient algorithm that takes the description of a binary labeling problem and outputs its distributed time complexity.
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The locality of a graph problem is the smallest distance $T$ such that each node can choose its own part of the solution based on its radius-$T$ neighborhood. In many settings, a graph problem can be solved efficiently with a distributed or parallel algorithm if and only if it has a small locality. In this work we seek to automate the study of solvability and locality: given the description of a graph problem $Pi$, we would like to determine if $Pi$ is solvable and what is the asymptotic locality of $Pi$ as a function of the size of the graph. Put otherwise, we seek to automatically synthesize efficient distributed and parallel algorithms for solving $Pi$. We focus on locally checkable graph problems; these are problems in which a solution is globally feasible if it looks feasible in all constant-radius neighborhoods. Prior work on such problems has brought primarily bad news: questions related to locality are undecidable in general, and even if we focus on the case of labeled paths and cycles, determining locality is $mathsf{PSPACE}$-hard (Balliu et al., PODC 2019). We complement prior negative results with efficient algorithms for the cases of unlabeled paths and cycles and, as an extension, for rooted trees. We introduce a new automata-theoretic perspective for studying locally checkable graph problems. We represent a locally checkable problem $Pi$ as a nondeterministic finite automaton $mathcal{M}$ over a unary alphabet. We identify polynomial-time-computable properties of the automaton $mathcal{M}$ that near-completely capture the solvability and locality of $Pi$ in cycles and paths, with the exception of one specific case that is $mbox{co-$mathsf{NP}$}$-complete.
We generalize the definition of Proof Labeling Schemes to reactive systems, that is, systems where the configuration is supposed to keep changing forever. As an example, we address the main classical test case of reactive tasks, namely, the task of token passing. Different RPLSs are given for the cases that the network is assumed to be a tree or an anonymous ring, or a general graph, and the sizes of RPLSs labels are analyzed. We also address the question of whether an RPLS exists. First, on the positive side, we show that there exists an RPLS for any distributed task for a family of graphs with unique identities. For the case of anonymous networks (even for the special case of rings), interestingly, it is known that no token passing algorithm is possible even if the number n of nodes is known. Nevertheless, we show that an RPLS is possible. On the negative side, we show that if one drops the assumption that n is known, then the construction becomes impossible.
Partitioning large matrices is an important problem in distributed linear algebra computing (used in ML among others). Briefly, our goal is to perform a sequence of matrix algebra operations in a distributed manner (whenever possible) on these large matrices. However, not all partitioning schemes work well with different matrix algebra operations and their implementations (algorithms). This is a type of data tiling problem. In this work we consider a theoretical model for a version of the matrix tiling problem in the setting of hypergraph labeling. We prove some hardness results and give a theoretical characterization of its complexity on random instances. Additionally we develop a greedy algorithm and experimentally show its efficacy.
A number of recent papers -- e.g. Brandt et al. (STOC 2016), Chang et al. (FOCS 2016), Ghaffari & Su (SODA 2017), Brandt et al. (PODC 2017), and Chang & Pettie (FOCS 2017) -- have advanced our understanding of one of the most fundamental questions in theory of distributed computing: what are the possible time complexity classes of LCL problems in the LOCAL model? In essence, we have a graph problem $Pi$ in which a solution can be verified by checking all radius-$O(1)$ neighbourhoods, and the question is what is the smallest $T$ such that a solution can be computed so that each node chooses its own output based on its radius-$T$ neighbourhood. Here $T$ is the distributed time complexity of $Pi$. The time complexity classes for deterministic algorithms in bounded-degree graphs that are known to exist by prior work are $Theta(1)$, $Theta(log^* n)$, $Theta(log n)$, $Theta(n^{1/k})$, and $Theta(n)$. It is also known that there are two gaps: one between $omega(1)$ and $o(log log^* n)$, and another between $omega(log^* n)$ and $o(log n)$. It has been conjectured that many more gaps exist, and that the overall time hierarchy is relatively simple -- indeed, this is known to be the case in restricted graph families such as cycles and grids. We show that the picture is much more diverse than previously expected. We present a general technique for engineering LCL problems with numerous different deterministic time complexities, including $Theta(log^{alpha}n)$ for any $alphage1$, $2^{Theta(log^{alpha}n)}$ for any $alphale 1$, and $Theta(n^{alpha})$ for any $alpha <1/2$ in the high end of the complexity spectrum, and $Theta(log^{alpha}log^* n)$ for any $alphage 1$, $smash{2^{Theta(log^{alpha}log^* n)}}$ for any $alphale 1$, and $Theta((log^* n)^{alpha})$ for any $alpha le 1$ in the low end; here $alpha$ is a positive rational number.
Superpixels have become prevalent in computer vision. They have been used to achieve satisfactory performance at a significantly smaller computational cost for various tasks. People have also combined superpixels with Markov random field (MRF) models. However, it often takes additional effort to formulate MRF on superpixel-level, and to the best of our knowledge there exists no principled approach to obtain this formulation. In this paper, we show how generic pixel-level binary MRF model can be solved in the superpixel space. As the main contribution of this paper, we show that a superpixel-level MRF can be derived from the pixel-level MRF by substituting the superpixel representation of the pixelwise label into the original pixel-level MRF energy. The resultant superpixel-level MRF energy also remains submodular for a submodular pixel-level MRF. The derived formula hence gives us a handy way to formulate MRF energy in superpixel-level. In the experiments, we demonstrate the efficacy of our approach on several computer vision problems.
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