Results 1  10
of
280
Randomized Algorithms
, 1995
"... Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simp ..."
Abstract

Cited by 1876 (38 self)
 Add to MetaCart
Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simplest, or both. A randomized algorithm is an algorithm that uses random numbers to influence the choices it makes in the course of its computation. Thus its behavior (typically quantified as running time or quality of output) varies from
Proportionate progress: A notion of fairness in resource allocation
 Algorithmica
, 1996
"... Given a set of n tasks and m resources, where each task x has a rational weight x:w = x:e=x:p; 0 < x:w < 1, a periodic schedule is one that allocates a resource to a task x for exactly x:e time units in each interval [x:p k; x:p (k + 1)) for all k 2 N. We de ne a notion of proportionate progress, ca ..."
Abstract

Cited by 241 (25 self)
 Add to MetaCart
Given a set of n tasks and m resources, where each task x has a rational weight x:w = x:e=x:p; 0 < x:w < 1, a periodic schedule is one that allocates a resource to a task x for exactly x:e time units in each interval [x:p k; x:p (k + 1)) for all k 2 N. We de ne a notion of proportionate progress, called Pfairness, and use it to design an e cient algorithm which solves the periodic scheduling problem. Keywords: Euclid's algorithm, fairness, network ow, periodic scheduling, resource allocation.
Linear programming in linear time when the dimension is fixed
 J. ACM
, 1984
"... Abstract. It is demonstrated that the linear programming problem in d variables and n constraints can be solved in O(n) time when d is fixed. This bound follows from a multidimensional search technique which is applicable for quadratic programming as well. There is also developed an algorithm that i ..."
Abstract

Cited by 194 (13 self)
 Add to MetaCart
Abstract. It is demonstrated that the linear programming problem in d variables and n constraints can be solved in O(n) time when d is fixed. This bound follows from a multidimensional search technique which is applicable for quadratic programming as well. There is also developed an algorithm that is polynomial in both n and d provided d is bounded by a certain slowly growing function of n. Categories and Subject Descriptors: F.2.1 [Analysis of Algorithms and Problem Complexity]: Numerical Algorithms and Problemscomputations on matrices; F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problemsgeometrical problems and computations; sorting and searching; G. 1.6 [Mathematics of Computing]: Optimizationlinear programming
Fast Algorithms for Sorting and Searching Strings
, 1997
"... We present theoretical algorithms for sorting and searching multikey data, and derive from them practical C implementations for applications in which keys are character strings. The sorting algorithm blends Quicksort and radix sort; it is competitive with the best known C sort codes. The searching a ..."
Abstract

Cited by 147 (0 self)
 Add to MetaCart
We present theoretical algorithms for sorting and searching multikey data, and derive from them practical C implementations for applications in which keys are character strings. The sorting algorithm blends Quicksort and radix sort; it is competitive with the best known C sort codes. The searching algorithm blends tries and binary search trees; it is faster than hashing and other commonly used search methods. The basic ideas behind the algorithms date back at least to the 1960s, but their practical utility has been overlooked. We also present extensions to more complex string problems, such as partialmatch searching. 1. Introduction Section 2 briefly reviews Hoare's [9] Quicksort and binary search trees. We emphasize a wellknown isomorphism relating the two, and summarize other basic facts. The multikey algorithms and data structures are presented in Section 3. Multikey Quicksort orders a set of n vectors with k components each. Like regular Quicksort, it partitions its input into...
How to Summarize the Universe: Dynamic Maintenance of Quantiles
 In VLDB
, 2002
"... Order statistics, i.e., quantiles, are frequently used in databases both at the database server as well as the application level. For example, they are useful in selectivity estimation during query optimization, in partitioning large relations, in estimating query result sizes when building us ..."
Abstract

Cited by 104 (13 self)
 Add to MetaCart
Order statistics, i.e., quantiles, are frequently used in databases both at the database server as well as the application level. For example, they are useful in selectivity estimation during query optimization, in partitioning large relations, in estimating query result sizes when building user interfaces, and in characterizing the data distribution of evolving datasets in the process of data mining.
Geometric Mesh Partitioning: Implementation and Experiments
"... We investigate a method of dividing an irregular mesh into equalsized pieces with few interconnecting edges. The method’s novel feature is that it exploits the geometric coordinates of the mesh vertices. It is based on theoretical work of Miller, Teng, Thurston, and Vavasis, who showed that certain ..."
Abstract

Cited by 102 (19 self)
 Add to MetaCart
We investigate a method of dividing an irregular mesh into equalsized pieces with few interconnecting edges. The method’s novel feature is that it exploits the geometric coordinates of the mesh vertices. It is based on theoretical work of Miller, Teng, Thurston, and Vavasis, who showed that certain classes of “wellshaped” finite element meshes have good separators. The geometric method is quite simple to implement: we describe a Matlab code for it in some detail. The method is also quite efficient and effective: we compare it with some other methods, including spectral bisection.
Fast Scheduling of Periodic Tasks on Multiple Resources
 In Proceedings of the 9th International Parallel Processing Symposium
"... Given n periodic tasks, each characterized by an execution requirement and a period, and m identical copies of a resource, the periodic scheduling problem is concerned with generating a schedule for the n tasks on the m resources. We present an algorithm that schedules every feasible instance of t ..."
Abstract

Cited by 100 (15 self)
 Add to MetaCart
Given n periodic tasks, each characterized by an execution requirement and a period, and m identical copies of a resource, the periodic scheduling problem is concerned with generating a schedule for the n tasks on the m resources. We present an algorithm that schedules every feasible instance of the periodic scheduling problem, and runs in O(minfm lg n; ng) time per slot scheduled. 1 Introduction Given a set \Gamma of n tasks, where each task x is characterized by two integer parameters x:e and x:p, and m identical copies of a resource, a periodic schedule is one that allocates a resource to each task x in \Gamma for exactly x:e time units in each interval [k \Delta x:p; (k+1) \Delta x:p) for all k in N, subject to the following constraints: Constraint 1: A resource can only be allocated to a task for an entire "slot" of time, where for each i in N slot i is the unit interval from time i to time i + 1. Constraint 2: No task may be allocated more than one copy of the resource ...
Random sampling techniques for space efficient online computation of order statistics of large datasets
 IN ACM SIGMOD '99
, 1999
"... In a recent paper [MRL98], we had described a general framework for single pass approximate quantile nding algorithms. This framework included several known algorithms as special cases. We had identi ed a new algorithm, within the framework, which had a signi cantly smaller requirement for main memo ..."
Abstract

Cited by 97 (1 self)
 Add to MetaCart
In a recent paper [MRL98], we had described a general framework for single pass approximate quantile nding algorithms. This framework included several known algorithms as special cases. We had identi ed a new algorithm, within the framework, which had a signi cantly smaller requirement for main memory than other known algorithms. In this paper, we address two issues left open in our earlier paper. First, all known and space e cient algorithms for approximate quantile nding require advance knowledge of the length of the input sequence. Many important database applications employing quantiles cannot provide this information. In this paper, we present anovel nonuniform random sampling scheme and an extension of our framework. Together, they form the basis of a new algorithm which computes approximate quantiles without knowing the input sequence length. Second, if the desired quantile is an extreme value (e.g., within the top 1 % of the elements), the space requirements of currently known algorithms are overly pessimistic. We provide a simple algorithm which estimates extreme values using less space than required by the earlier more general technique for computing all quantiles. Our principal observation here is that random sampling is quanti ably better when estimating extreme values than is the case with the median.
Approximate counts and quantiles over sliding windows
 In PODS
, 2004
"... We consider the problem of maintaining ɛapproximate counts and quantiles over a stream sliding window using limited space. We consider two types of sliding windows depending on whether the number of elements N in the window is fixed (fixedsize sliding window) or variable (variablesize sliding win ..."
Abstract

Cited by 82 (1 self)
 Add to MetaCart
We consider the problem of maintaining ɛapproximate counts and quantiles over a stream sliding window using limited space. We consider two types of sliding windows depending on whether the number of elements N in the window is fixed (fixedsize sliding window) or variable (variablesize sliding window). In a fixedsize sliding window, both the ends of the window slide synchronously over the stream. In a variablesize sliding window, an adversary slides the window ends independently, and therefore has the ability to vary the number of elements N in the window. We present various deterministic and randomized algorithms for approximate counts and quantiles. All of our algorithms require O ( 1 1 polylog ( , N)) space. For quantiles, this space ɛ ɛ requirement is an improvement over the previous best bound of O ( 1 ɛ2 polylog ( 1, N)). We believe that no previous work ɛ on spaceefficient approximate counts over sliding windows exists. 1.