23 research outputs found
Online Dominating Set
This paper is devoted to the online dominating set problem and its variants on trees, bipartite, bounded-degree, planar, and general graphs, distinguishing between connected and not necessarily connected graphs. We believe this paper represents the first systematic study of the effect of two limitations of online algorithms: making irrevocable decisions while not knowing the future, and being incremental, i.e., having to maintain solutions to all prefixes of the input. This is quantified through competitive analyses of online algorithms against two optimal algorithms, both knowing the entire input, but only one having to be incremental. We also consider the competitive ratio of the weaker of the two optimal algorithms against the other. In most cases, we obtain tight bounds on the competitive ratios. Our results show that requiring the graphs to be presented in a connected fashion allows the online algorithms to obtain provably better solutions. Furthermore, we get detailed information regarding the significance of the necessary requirement that online algorithms be incremental. In some cases, having to be incremental fully accounts for the online algorithm\u27s disadvantage
Adding Isolated Vertices Makes some Online Algorithms Optimal
An unexpected difference between online and offline algorithms is observed.
The natural greedy algorithms are shown to be worst case online optimal for
Online Independent Set and Online Vertex Cover on graphs with 'enough' isolated
vertices, Freckle Graphs. For Online Dominating Set, the greedy algorithm is
shown to be worst case online optimal on graphs with at least one isolated
vertex. These algorithms are not online optimal in general. The online
optimality results for these greedy algorithms imply optimality according to
various worst case performance measures, such as the competitive ratio. It is
also shown that, despite this worst case optimality, there are Freckle graphs
where the greedy independent set algorithm is objectively less good than
another algorithm. It is shown that it is NP-hard to determine any of the
following for a given graph: the online independence number, the online vertex
cover number, and the online domination number.Comment: A footnote in the .tex file didn't show up in the last version. This
was fixe
The Advice Complexity of a Class of Hard Online Problems
The advice complexity of an online problem is a measure of how much knowledge
of the future an online algorithm needs in order to achieve a certain
competitive ratio. Using advice complexity, we define the first online
complexity class, AOC. The class includes independent set, vertex cover,
dominating set, and several others as complete problems. AOC-complete problems
are hard, since a single wrong answer by the online algorithm can have
devastating consequences. For each of these problems, we show that
bits of advice are
necessary and sufficient (up to an additive term of ) to achieve a
competitive ratio of .
The results are obtained by introducing a new string guessing problem related
to those of Emek et al. (TCS 2011) and B\"ockenhauer et al. (TCS 2014). It
turns out that this gives a powerful but easy-to-use method for providing both
upper and lower bounds on the advice complexity of an entire class of online
problems, the AOC-complete problems.
Previous results of Halld\'orsson et al. (TCS 2002) on online independent
set, in a related model, imply that the advice complexity of the problem is
. Our results improve on this by providing an exact formula for
the higher-order term. For online disjoint path allocation, B\"ockenhauer et
al. (ISAAC 2009) gave a lower bound of and an upper bound of
on the advice complexity. We improve on the upper bound by a
factor of . For the remaining problems, no bounds on their advice
complexity were previously known.Comment: Full paper to appear in Theory of Computing Systems. A preliminary
version appeared in STACS 201
Relaxing the Irrevocability Requirement for Online Graph Algorithms
Online graph problems are considered in models where the irrevocability
requirement is relaxed. Motivated by practical examples where, for example,
there is a cost associated with building a facility and no extra cost
associated with doing it later, we consider the Late Accept model, where a
request can be accepted at a later point, but any acceptance is irrevocable.
Similarly, we also consider a Late Reject model, where an accepted request can
later be rejected, but any rejection is irrevocable (this is sometimes called
preemption). Finally, we consider the Late Accept/Reject model, where late
accepts and rejects are both allowed, but any late reject is irrevocable. For
Independent Set, the Late Accept/Reject model is necessary to obtain a constant
competitive ratio, but for Vertex Cover the Late Accept model is sufficient and
for Minimum Spanning Forest the Late Reject model is sufficient. The Matching
problem has a competitive ratio of 2, but in the Late Accept/Reject model, its
competitive ratio is 3/2
Optimal Metric Search Is Equivalent to the Minimum Dominating Set Problem
In metric search, worst-case analysis is of little value, as the search
invariably degenerates to a linear scan for ill-behaved data. Consequently,
much effort has been expended on more nuanced descriptions of what performance
might in fact be attainable, including heuristic baselines like the AESA
family, as well as statistical proxies such as intrinsic dimensionality. This
paper gets to the heart of the matter with an exact characterization of the
best performance actually achievable for any given data set and query.
Specifically, linear-time objective-preserving reductions are established in
both directions between optimal metric search and the minimum dominating set
problem, whose greedy approximation becomes the equivalent of an oracle-based
AESA, repeatedly selecting the pivot that eliminates the most of the remaining
points. As an illustration, the AESA heuristic is adapted to downplay the role
of previously eliminated points, yielding some modest performance improvements
over the original, as well as its younger relative iAESA2
Pick, Pack, & Survive: Charging Robots in a Modern Warehouse based on Online Connected Dominating Sets
The modern warehouse is partially automated by robots. Instead of letting human workers walk into shelfs and pick up the required stock, big groups of autonomous mobile robots transport the inventory to the workers. Typically, these robots have an electric drive and need to recharge frequently during the day. When we scale this approach up, it is essential to place recharging stations strategically and as soon as needed so that all robots can survive. In this work, we represent a warehouse topology by a graph and address this challenge with the Online Connected Dominating Set problem (OCDS), an online variant of the classical Connected Dominating Set problem [Guha and Khuller, 1998]. We are given an undirected connected graph G = (V, E) and a sequence of subsets of V arriving over time. The goal is to grow a connected subgraph that dominates all arriving nodes and contains as few nodes as possible. We propose an O(log^2 n)-competitive randomized algorithm for OCDS in general graphs, where n is the number of nodes in the input graph. This is the best one can achieve due to Korman\u27s randomized lower bound of Omega(log n log m) [Korman, 2005] for the related Online Set Cover problem [Alon et al., 2003], where n is the number of elements and m is the number of subsets. We also run extensive simulations to show that our algorithm performs well in a simulated warehouse, where the topology of a warehouse is modeled as a randomly generated geometric graph
Online Class Cover Problem
In this paper, we study the online class cover problem where a (finite or
infinite) family of geometric objects and a set of red
points in are given a prior, and blue points from
arrives one after another. Upon the arrival of a blue point, the online
algorithm must make an irreversible decision to cover it with objects from
that do not cover any points of . The objective of the
problem is to place the minimum number of objects. When consists of
all possible translates of a square in , we prove that the
competitive ratio of any deterministic online algorithm is . On the other hand, when the objects are all possible translates of a
rectangle in , we propose an -competitive
deterministic algorithm for the problem.Comment: 27 pages, 23 figure