55 research outputs found
Fast Monotone Summation over Disjoint Sets
We study the problem of computing an ensemble of multiple sums where the
summands in each sum are indexed by subsets of size of an -element
ground set. More precisely, the task is to compute, for each subset of size
of the ground set, the sum over the values of all subsets of size that are
disjoint from the subset of size . We present an arithmetic circuit that,
without subtraction, solves the problem using arithmetic
gates, all monotone; for constant , this is within the factor
of the optimal. The circuit design is based on viewing the summation as a "set
nucleation" task and using a tree-projection approach to implement the
nucleation. Applications include improved algorithms for counting heaviest
-paths in a weighted graph, computing permanents of rectangular matrices,
and dynamic feature selection in machine learning
Deterministic Subgraph Detection in Broadcast CONGEST
We present simple deterministic algorithms for subgraph finding and enumeration in the broadcast CONGEST model of distributed computation:
- For any constant k, detecting k-paths and trees on k nodes can be done in O(1) rounds.
- For any constant k, detecting k-cycles and pseudotrees on k nodes can be done in O(n)
rounds.
- On d-degenerate graphs, cliques and 4-cycles can be enumerated in O(d + log n) rounds, and
5-cycles in O(d2 + log n) rounds.
In many cases, these bounds are tight up to logarithmic factors. Moreover, we show that the algorithms for d-degenerate graphs can be improved to O(d/logn) and O(d2/logn), respect- ively, in the supported CONGEST model, which can be seen as an intermediate model between CONGEST and the congested clique
Towards Tight Communication Lower Bounds for Distributed Optimisation
We consider a standard distributed optimisation setting where machines,
each holding a -dimensional function , aim to jointly minimise the sum
of the functions . This problem arises naturally in
large-scale distributed optimisation, where a standard solution is to apply
variants of (stochastic) gradient descent. We focus on the communication
complexity of this problem: our main result provides the first fully
unconditional bounds on total number of bits which need to be sent and received
by the machines to solve this problem under point-to-point communication,
within a given error-tolerance. Specifically, we show that total bits need to be communicated between the machines to find
an additive -approximation to the minimum of . The result holds for both deterministic and randomised algorithms, and,
importantly, requires no assumptions on the algorithm structure. The lower
bound is tight under certain restrictions on parameter values, and is matched
within constant factors for quadratic objectives by a new variant of quantised
gradient descent, which we describe and analyse. Our results bring over tools
from communication complexity to distributed optimisation, which has potential
for further applications
Beyond Distributed Subgraph Detection: Induced Subgraphs, Multicolored Problems and Graph Parameters
Subgraph detection has recently been one of the most studied problems in the CONGEST model of distributed computing. In this work, we study the distributed complexity of problems closely related to subgraph detection, mainly focusing on induced subgraph detection. The main line of this work presents lower bounds and parameterized algorithms w.r.t structural parameters of the input graph:
- On general graphs, we give unconditional lower bounds for induced detection of cycles and patterns of treewidth 2 in CONGEST. Moreover, by adapting reductions from centralized parameterized complexity, we prove lower bounds in CONGEST for detecting patterns with a 4-clique, and for induced path detection conditional on the hardness of triangle detection in the congested clique.
- On graphs of bounded degeneracy, we show that induced paths can be detected fast in CONGEST using techniques from parameterized algorithms, while detecting cycles and patterns of treewidth 2 is hard.
- On graphs of bounded vertex cover number, we show that induced subgraph detection is easy in CONGEST for any pattern graph. More specifically, we adapt a centralized parameterized algorithm for a more general maximum common induced subgraph detection problem to the distributed setting. In addition to these induced subgraph detection results, we study various related problems in the CONGEST and congested clique models, including for multicolored versions of subgraph-detection-like problems
New Classes of Distributed Time Complexity
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 in which a solution can be verified by checking all
radius- neighbourhoods, and the question is what is the smallest such
that a solution can be computed so that each node chooses its own output based
on its radius- neighbourhood. Here is the distributed time complexity of
.
The time complexity classes for deterministic algorithms in bounded-degree
graphs that are known to exist by prior work are , , , , and . It is also known
that there are two gaps: one between and , and
another between and . 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
for any , for any , and
for any in the high end of the complexity
spectrum, and for any ,
for any , and
for any in the low end; here
is a positive rational number
Bayesian Network Structure Learning with Integer Programming : Polytopes, Facets and Complexity
Peer reviewe
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