ﻻ يوجد ملخص باللغة العربية
The area of computing with uncertainty considers problems where some information about the input elements is uncertain, but can be obtained using queries. For example, instead of the weight of an element, we may be given an interval that is guaranteed to contain the weight, and a query can be performed to reveal the weight. While previous work has considered models where queries are asked either sequentially (adaptive model) or all at once (non-adaptive model), and the goal is to minimize the number of queries that are needed to solve the given problem, we propose and study a new model where $k$ queries can be made in parallel in each round, and the goal is to minimize the number of query rounds. We use competitive analysis and present upper and lower bounds on the number of query rounds required by any algorithm in comparison with the optimal number of query rounds. Given a set of uncertain elements and a family of $m$ subsets of that set, we present an algorithm for determining the value of the minimum of each of the subsets that requires at most $(2+varepsilon) cdot mathrm{opt}_k+mathrm{O}left(frac{1}{varepsilon} cdot lg mright)$ rounds for every $0<varepsilon<1$, where $mathrm{opt}_k$ is the optimal number of rounds, as well as nearly matching lower bounds. For the problem of determining the $i$-th smallest value and identifying all elements with that value in a set of uncertain elements, we give a $2$-round-competitive algorithm. We also show that the problem of sorting a family of sets of uncertain elements admits a $2$-round-competitive algorithm and this is the best possible.
For over a decade now we have been witnessing the success of {em massive parallel computation} (MPC) frameworks, such as MapReduce, Hadoop, Dryad, or Spark. One of the reasons for their success is the fact that these frameworks are able to accurately
Let $G$ be an $n$-vertex graph with $m$ edges. When asked a subset $S$ of vertices, a cut query on $G$ returns the number of edges of $G$ that have exactly one endpoint in $S$. We show that there is a bounded-error quantum algorithm that determines a
We study the problem of sorting under incomplete information, when queries are used to resolve uncertainties. Each of $n$ data items has an unknown value, which is known to lie in a given interval. We can pay a query cost to learn the actual value, a
We consider the following stochastic matching problem on both weighted and unweighted graphs: A graph $G(V, E)$ along with a parameter $p in (0, 1)$ is given in the input. Each edge of $G$ is realized independently with probability $p$. The goal is t
This paper presents universal algorithms for clustering problems, including the widely studied $k$-median, $k$-means, and $k$-center objectives. The input is a metric space containing all potential client locations. The algorithm must select $k$ clus