6,533 research outputs found
The Online Disjoint Set Cover Problem and its Applications
Given a universe of elements and a collection of subsets
of , the maximum disjoint set cover problem (DSCP) is to
partition into as many set covers as possible, where a set cover
is defined as a collection of subsets whose union is . We consider the
online DSCP, in which the subsets arrive one by one (possibly in an order
chosen by an adversary), and must be irrevocably assigned to some partition on
arrival with the objective of minimizing the competitive ratio. The competitive
ratio of an online DSCP algorithm is defined as the maximum ratio of the
number of disjoint set covers obtained by the optimal offline algorithm to the
number of disjoint set covers obtained by across all inputs. We propose an
online algorithm for solving the DSCP with competitive ratio . We then
show a lower bound of on the competitive ratio for any
online DSCP algorithm. The online disjoint set cover problem has wide ranging
applications in practice, including the online crowd-sourcing problem, the
online coverage lifetime maximization problem in wireless sensor networks, and
in online resource allocation problems.Comment: To appear in IEEE INFOCOM 201
Optimal file allocation problems for distributed data bases in unreliable computer networks
"December, 1982"Bibliography: leaf [6]"ONR/N00014-77-C-0532 (NR 041-519)Moses Ma
Decision-based genetic algorithms for solving multi-period project scheduling with dynamically experienced workforce
The importance of the flexibility of resources increased rapidly with the turbulent changes in the industrial context, to meet the customersâ requirements. Among all resources, the most important and considered as the hardest to manage are human resources, in reasons of availability and/or conventions. In this article, we present an approach to solve project scheduling with multi-period human resources allocation taking into account two flexibility levers. The first is the annual hours and working time regulation, and the second is the actorsâ multi-skills. The productivity of each operator was considered as dynamic, developing or degrading depending on the prior allocation decisions. The solving approach mainly uses decision-based genetic algorithms, in which, chromosomes donât represent directly the problem solution; they simply present three decisions: tasksâ priorities for execution, actorsâ priorities for carrying out these tasks, and finally the priority of working time strategy that can be considered during the specified working period. Also the principle of critical skill was taken into account. Based on these decisions and during a serial scheduling generating scheme, one can in a sequential manner introduce the project scheduling and the corresponding workforce allocations
An Introduction to Mechanized Reasoning
Mechanized reasoning uses computers to verify proofs and to help discover new
theorems. Computer scientists have applied mechanized reasoning to economic
problems but -- to date -- this work has not yet been properly presented in
economics journals. We introduce mechanized reasoning to economists in three
ways. First, we introduce mechanized reasoning in general, describing both the
techniques and their successful applications. Second, we explain how mechanized
reasoning has been applied to economic problems, concentrating on the two
domains that have attracted the most attention: social choice theory and
auction theory. Finally, we present a detailed example of mechanized reasoning
in practice by means of a proof of Vickrey's familiar theorem on second-price
auctions
Program Verification of Numerical Computation
These notes outline a formal method for program verification of numerical
computation. It forms the basis of the software package VPC in its initial
phase of development. Much of the style of presentation is in the form of notes
that outline the definitions and rules upon which VPC is based. The initial
motivation of this project was to address some practical issues of computation,
especially of numerically intensive programs that are commonplace in computer
models. The project evolved into a wider area for program construction as
proofs leading to a model of inference in a more general sense. Some basic
results of machine arithmetic are derived as a demonstration of VPC
Validating Sample Average Approximation Solutions with Negatively Dependent Batches
Sample-average approximations (SAA) are a practical means of finding
approximate solutions of stochastic programming problems involving an extremely
large (or infinite) number of scenarios. SAA can also be used to find estimates
of a lower bound on the optimal objective value of the true problem which, when
coupled with an upper bound, provides confidence intervals for the true optimal
objective value and valuable information about the quality of the approximate
solutions. Specifically, the lower bound can be estimated by solving multiple
SAA problems (each obtained using a particular sampling method) and averaging
the obtained objective values. State-of-the-art methods for lower-bound
estimation generate batches of scenarios for the SAA problems independently. In
this paper, we describe sampling methods that produce negatively dependent
batches, thus reducing the variance of the sample-averaged lower bound
estimator and increasing its usefulness in defining a confidence interval for
the optimal objective value. We provide conditions under which the new sampling
methods can reduce the variance of the lower bound estimator, and present
computational results to verify that our scheme can reduce the variance
significantly, by comparison with the traditional Latin hypercube approach
A GPU Simulation for Evolution-Communication P Systems with Energy Having no Antiport Rules
Evolution-Communication P system with energy (ECPe systems) is a cell-
like variant P system which establishes a dependence between evolution and communi-
cation through special objects, called `energy,' produced during evolution and utilized
during communication. This paper presents our initial progress and e orts on the im-
plementation and simulation of ECPe systems using Graphics Processing Units (GPUs).
Our implementation uses matrix representation and operations presented in a previous
work. Speci cally, an implementation of computations on ECPe systems without antiport
rules is discussed.Junta de AndalucĂa P08-TIC-04200Ministerio de Ciencia e InnovaciĂłn TIN2012-3743
A threshold based dynamic data allocation algorithm - a Markov Chain model approach
In this study, a new dynamic data allocation algorithm for non-replicated Distributed Database Systems (DDS), namely the threshold algorithm, is formulated and proposed. The threshold algorithm reallocates data with respect to changing data access patterns. The proposed algorithm is distributed in the sense that each node autonomously decides whether to transfer the ownership of a fragment in DDS to another node or not. The transfer decision depends on the past accesses of the fragment. Each fragment continuously migrates ftom the node where it is not accessed locally more than a certain number of past accesses, namely a threshold value. The threshold algorithm is modeled for a fragment of the database as a finite Markov chain with constant node access probabilities. In the model, a special case, where all nodes have equal access probabilities except one with a different access probability, is analyzed. It has been shown that for positive threshold values the fragment will tend to remain at the node with the higher access probability. It is also shown that the greater the threshold values are, the greater the tendency of the fragment to remain at the node with higher access probability will be. The threshold algorithm is especially suitable for a DDS where data access pattern changes dynamically
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