1,861 research outputs found
Approximation of non-boolean 2CSP
We develop a polynomial time
approximate algorithm for Max 2CSP-, the problem where we are given a
collection of constraints, each involving two variables, where each variable
ranges over a set of size , and we want to find an assignment to the
variables that maximizes the number of satisfied constraints. Assuming the
Unique Games Conjecture, this is the best possible approximation up to constant
factors.
Previously, a -approximate algorithm was known, based on linear
programming. Our algorithm is based on semidefinite programming (SDP) and on a
novel rounding technique. The SDP that we use has an almost-matching
integrality gap
How to Play Unique Games against a Semi-Random Adversary
In this paper, we study the average case complexity of the Unique Games
problem. We propose a natural semi-random model, in which a unique game
instance is generated in several steps. First an adversary selects a completely
satisfiable instance of Unique Games, then she chooses an epsilon-fraction of
all edges, and finally replaces ("corrupts") the constraints corresponding to
these edges with new constraints. If all steps are adversarial, the adversary
can obtain any (1-epsilon) satisfiable instance, so then the problem is as hard
as in the worst case. In our semi-random model, one of the steps is random, and
all other steps are adversarial. We show that known algorithms for unique games
(in particular, all algorithms that use the standard SDP relaxation) fail to
solve semi-random instances of Unique Games.
We present an algorithm that with high probability finds a solution
satisfying a (1-delta) fraction of all constraints in semi-random instances (we
require that the average degree of the graph is Omega(log k). To this end, we
consider a new non-standard SDP program for Unique Games, which is not a
relaxation for the problem, and show how to analyze it. We present a new
rounding scheme that simultaneously uses SDP and LP solutions, which we believe
is of independent interest.
Our result holds only for epsilon less than some absolute constant. We prove
that if epsilon > 1/2, then the problem is hard in one of the models, the
result assumes the 2-to-2 conjecture.
Finally, we study semi-random instances of Unique Games that are at most
(1-epsilon) satisfiable. We present an algorithm that with high probability,
distinguishes between the case when the instance is a semi-random instance and
the case when the instance is an (arbitrary) (1-delta) satisfiable instance if
epsilon > c delta
Collaborative Learning of Stochastic Bandits over a Social Network
We consider a collaborative online learning paradigm, wherein a group of
agents connected through a social network are engaged in playing a stochastic
multi-armed bandit game. Each time an agent takes an action, the corresponding
reward is instantaneously observed by the agent, as well as its neighbours in
the social network. We perform a regret analysis of various policies in this
collaborative learning setting. A key finding of this paper is that natural
extensions of widely-studied single agent learning policies to the network
setting need not perform well in terms of regret. In particular, we identify a
class of non-altruistic and individually consistent policies, and argue by
deriving regret lower bounds that they are liable to suffer a large regret in
the networked setting. We also show that the learning performance can be
substantially improved if the agents exploit the structure of the network, and
develop a simple learning algorithm based on dominating sets of the network.
Specifically, we first consider a star network, which is a common motif in
hierarchical social networks, and show analytically that the hub agent can be
used as an information sink to expedite learning and improve the overall
regret. We also derive networkwide regret bounds for the algorithm applied to
general networks. We conduct numerical experiments on a variety of networks to
corroborate our analytical results.Comment: 14 Pages, 6 Figure
On the Expansion of Group-Based Lifts
A -lift of an -vertex base graph is a graph on
vertices, where each vertex of is replaced by vertices
and each edge in is replaced by a matching
representing a bijection so that the edges of are of the form
. Lifts have been studied as a means to efficiently
construct expanders. In this work, we study lifts obtained from groups and
group actions. We derive the spectrum of such lifts via the representation
theory principles of the underlying group. Our main results are:
(1) There is a constant such that for every , there
does not exist an abelian -lift of any -vertex -regular base graph
with being almost Ramanujan (nontrivial eigenvalues of the adjacency matrix
at most in magnitude). This can be viewed as an analogue of the
well-known no-expansion result for abelian Cayley graphs.
(2) A uniform random lift in a cyclic group of order of any -vertex
-regular base graph , with the nontrivial eigenvalues of the adjacency
matrix of bounded by in magnitude, has the new nontrivial
eigenvalues also bounded by in magnitude with probability
. In particular, there is a constant such that for
every , there exists a lift of every Ramanujan graph in
a cyclic group of order with being almost Ramanujan. We use this to
design a quasi-polynomial time algorithm to construct almost Ramanujan
expanders deterministically.
The existence of expanding lifts in cyclic groups of order
can be viewed as a lower bound on the order of the largest abelian group
that produces expanding lifts. Our results show that the lower bound matches
the upper bound for (upto in the exponent)
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Scalar dissipation rate based flamelet modelling of turbulent premixed flames
Lean premixed combustion has potential for reducing emissions from combustion devices without compromising fuel efficiency, but it is prone to instabilities which presents design difficulties. From emissions point of view reliable predictions of species formation rates in the flame zone are required while from the point of view of thermo--acoustics the prediction of spatial variation of heat release rate is crucial; both tasks are challenging but imperative in CFD based design of combustion systems. In this thesis a computational model for turbulent premixed combustion is proposed in the RANS framework and its predictive ability is studied.
The model is based on the flamelet concept and employs strained laminar flamelets in reactant--to--product opposed flow configuration. The flamelets are parametrised by scalar dissipation rate of progress variable which is a suitable quantity to describe the flamelet structure since it is governed by convection--diffusion--reaction balance and represents the flame front dynamics. This paramaterisation is new. The mean reaction rate and mean species concentrations are obtained by integrating the corresponding flamelets quantity weighted by the joint pdf of the progress variable and its dissipation rate. The marginal pdf of the progress variable is obtained using --pdf and the pdf of the conditional dissipation rate is presumed to be log--normal. The conditional mean dissipation rate is obtained from unconditional mean dissipation rate which is a modelling parameter. An algebraic model for the unconditional mean scalar dissipation rate is proposed based on the relevant physics of reactive scalar mixing in turbulent premixed flames. This algebraic model is validated directly using DNS data. An indirect validation is performed by deriving a turbulent flame speed expression using the Kolmogorov--Petrovskii--Piskunov analysis and comparing its predictions with experimental data from a wide range of flame and flow conditions.
The mean reaction rate closure of the strained flamelets model is assessed using RANS calculations of statistically planar one--dimensional flames in corrugated flamelets and thin reaction zones regimes. The flame speeds predicted by this closure were close to experimental data in both the regimes. On the other hand, an unstrained flamelets closure predicts flame speed close to the experimental data in the corrugated flamelets regime, but overpredicts in the thin reaction zones regime indicating an overprediction of the mean reaction rate.
The overall predictive ability of the strained flamelets model is assessed via calculations of laboratory flames of two different configurations: a rod stabilised V--flame and pilot stabilised Bunsen flames. For the V--flame, whose conditions correspond to the corrugated flamelets regime, the strained and unstrained flamelets models yield similar predictions which are in good agreement with experimental measurements. For the Bunsen flames which are in the thin reaction zones regime, the unstrained flamelet model predicts a smaller flame brush while the predictions of the strained flamelets model are in good agreement with the experimental data. The major and minor species concentrations are also reasonably well predicted by the strained flamelets model, although the minor species predictions seem sensitive to the product stream composition of the laminar flamelets.
The fluid dynamics induced attenuation of the reaction rate is captured by the strained flamelets model enabling it to give better predictions than the unstrained flamelets model in the thin reaction zones regime. The planar flames and laboratory flames calculations illustrate the importance of appropriately accounting for fluid dynamic effects on flamelet structure and the scalar dissipation rate based strained flamelet model seems promising in this respect. Furthermore, this model seems to have a wide range of applicability with a fixed set of model parameters.This doctoral work was supported by a scholarship awarded by the Nehru Cambridge Trust of Cambridge Commonwealth Trusts
Approaching Author Identity through First-person Pronouns and Metadiscourse : A study of opinion articles in US news media
This study combines metadiscourse research and sociolinguistic methods to establish which social variables influence the choice of metadiscourse resources containing first-person pronouns in US opinion news texts.
The study has three main goals. The first goal is to establish which first-person pronouns are used by the authors of opinion articles, and which social variables influence or at least correlate with their choice of first-person pronouns the most, as well as to study the contexts in which these pronouns are used. The second goal is to establish which metadiscourse resources and to what extent are used by the authors of different social groups. The third goal is to establish if there is any correlation between various social factors and the use of particular metadiscourse resources. The corpus for the study was collected from articles posted on the sites of eleven US news publishers and consists of op-ed texts on politics and social issues along with the information about the authors of these texts including gender, age, ethnic background, education, and occupation. To fulfill these goals the study uses corpus linguistics methods for calculating and comparing the occurrence frequencies of first-person pronouns by social variables and Ken Hyland's interpersonal model of metadiscourse.
The results show that social variables do indeed significantly correlate with the choice of first-person pronouns and the metadiscourse resources containing these pronouns. The pronouns that are mostly used are the subject pronouns I and we, the mostly used metadiscourse resources being Self-mentions and Engagement markers. The most prominent social variables that correlate with the use of pronouns are gender and, to a lesser degree, occupation. The female authors of the articles in the corpus use more first-person pronouns than male authors and show a preference for first-person singular pronouns and plural inclusive pronouns while male authors use more first-person plural pronouns. The most noticeable difference in pronoun usage between genders can be observed between male and female journalists; however, journalists of one gender do not differ from each other in either pronoun or metadiscourse use with other factors being equal
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