125,892 research outputs found
Solving the At-Most-Once Problem with Nearly Optimal Effectiveness
We present and analyze a wait-free deterministic algorithm for solving the
at-most-once problem: how m shared-memory fail-prone processes perform
asynchronously n jobs at most once. Our algorithmic strategy provides for the
first time nearly optimal effectiveness, which is a measure that expresses the
total number of jobs completed in the worst case. The effectiveness of our
algorithm equals n-2m+2. This is up to an additive factor of m close to the
known effectiveness upper bound n-m+1 over all possible algorithms and improves
on the previously best known deterministic solutions that have effectiveness
only n-log m o(n). We also present an iterative version of our algorithm that
for any is both
effectiveness-optimal and work-optimal, for any constant . We
then employ this algorithm to provide a new algorithmic solution for the
Write-All problem which is work optimal for any
.Comment: Updated Version. A Brief Announcement was published in PODC 2011. An
Extended Abstract was published in the proceeding of ICDCN 2012. A full
version was published in Theoretical Computer Science, Volume 496, 22 July
2013, Pages 69 - 8
Optimal mistuning for enhanced aeroelastic stability of transonic fans
An inverse design procedure was developed for the design of a mistuned rotor. The design requirements are that the stability margin of the eigenvalues of the aeroelastic system be greater than or equal to some minimum stability margin, and that the mass added to each blade be positive. The objective was to achieve these requirements with a minimal amount of mistuning. Hence, the problem was posed as a constrained optimization problem. The constrained minimization problem was solved by the technique of mathematical programming via augmented Lagrangians. The unconstrained minimization phase of this technique was solved by the variable metric method. The bladed disk was modelled as being composed of a rigid disk mounted on a rigid shaft. Each of the blades were modelled with a single tosional degree of freedom
Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model
Resource allocation is the process of optimizing the rare resources. In the
area of security, how to allocate limited resources to protect a massive number
of targets is especially challenging. This paper addresses this resource
allocation issue by constructing a game theoretic model. A defender and an
attacker are players and the interaction is formulated as a trade-off between
protecting targets and consuming resources. The action cost which is a
necessary role of consuming resource, is considered in the proposed model.
Additionally, a bounded rational behavior model (Quantal Response, QR), which
simulates a human attacker of the adversarial nature, is introduced to improve
the proposed model. To validate the proposed model, we compare the different
utility functions and resource allocation strategies. The comparison results
suggest that the proposed resource allocation strategy performs better than
others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference
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