5,500 research outputs found
Parameterized Construction of Program Representations for Sparse Dataflow Analyses
Data-flow analyses usually associate information with control flow regions.
Informally, if these regions are too small, like a point between two
consecutive statements, we call the analysis dense. On the other hand, if these
regions include many such points, then we call it sparse. This paper presents a
systematic method to build program representations that support sparse
analyses. To pave the way to this framework we clarify the bibliography about
well-known intermediate program representations. We show that our approach, up
to parameter choice, subsumes many of these representations, such as the SSA,
SSI and e-SSA forms. In particular, our algorithms are faster, simpler and more
frugal than the previous techniques used to construct SSI - Static Single
Information - form programs. We produce intermediate representations isomorphic
to Choi et al.'s Sparse Evaluation Graphs (SEG) for the family of data-flow
problems that can be partitioned per variables. However, contrary to SEGs, we
can handle - sparsely - problems that are not in this family
Probabilistic data flow analysis: a linear equational approach
Speculative optimisation relies on the estimation of the probabilities that
certain properties of the control flow are fulfilled. Concrete or estimated
branch probabilities can be used for searching and constructing advantageous
speculative and bookkeeping transformations.
We present a probabilistic extension of the classical equational approach to
data-flow analysis that can be used to this purpose. More precisely, we show
how the probabilistic information introduced in a control flow graph by branch
prediction can be used to extract a system of linear equations from a program
and present a method for calculating correct (numerical) solutions.Comment: In Proceedings GandALF 2013, arXiv:1307.416
Long's Vortex Revisited
The conical self-similar vortex solution of Long (1961) is reconsidered, with
a view toward understanding what, if any, relationship exists between Long's
solution and the more-recent similarity solutions of Mayer and Powell (1992),
which are a rotational-flow analogue of the Falkner-Skan boundary-layer flows,
describing a self-similar axisymmetric vortex embedded in an external stream
whose axial velocity varies as a power law in the axial (z) coordinate, with
phi=r/z^n being the radial similarity coordinate and n the core growth rate
parameter. We show that, when certain ostensible differences in the
formulations and radial scalings are properly accounted for, the Long and
Mayer-Powell flows in fact satisfy the same system of coupled ordinary
differential equations, subject to different kinds of outer-boundary
conditions, and with Long's equations a special case corresponding to conical
vortex core growth, n=1 with outer axial velocity field decelerating in a 1/z
fashion, which implies a severe adverse pressure gradient. For pressure
gradients this adverse Mayer and Powell were unable to find any
leading-edge-type vortex flow solutions which satisfy a basic physicality
criterion based on monotonicity of the total-pressure profile of the flow, and
it is shown that Long's solutions also violate this criterion, in an extreme
fashion. Despite their apparent nonphysicality, the fact that Long's solutions
fit into a more general similarity framework means that nonconical analogues of
these flows should exist. The far-field asymptotics of these generalized
solutions are derived and used as the basis for a hybrid spectral-numerical
solution of the generalized similarity equations, which reveal the existence of
solutions for more modestly adverse pressure gradients than those in Long's
case, and which do satisfy the above physicality criterion.Comment: 30 pages, including 16 figure
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness
Algorithms as Mechanisms: The Price of Anarchy of Relax-and-Round
Many algorithms that are originally designed without explicitly considering
incentive properties are later combined with simple pricing rules and used as
mechanisms. The resulting mechanisms are often natural and simple to
understand. But how good are these algorithms as mechanisms? Truthful reporting
of valuations is typically not a dominant strategy (certainly not with a
pay-your-bid, first-price rule, but it is likely not a good strategy even with
a critical value, or second-price style rule either). Our goal is to show that
a wide class of approximation algorithms yields this way mechanisms with low
Price of Anarchy.
The seminal result of Lucier and Borodin [SODA 2010] shows that combining a
greedy algorithm that is an -approximation algorithm with a
pay-your-bid payment rule yields a mechanism whose Price of Anarchy is
. In this paper we significantly extend the class of algorithms for
which such a result is available by showing that this close connection between
approximation ratio on the one hand and Price of Anarchy on the other also
holds for the design principle of relaxation and rounding provided that the
relaxation is smooth and the rounding is oblivious.
We demonstrate the far-reaching consequences of our result by showing its
implications for sparse packing integer programs, such as multi-unit auctions
and generalized matching, for the maximum traveling salesman problem, for
combinatorial auctions, and for single source unsplittable flow problems. In
all these problems our approach leads to novel simple, near-optimal mechanisms
whose Price of Anarchy either matches or beats the performance guarantees of
known mechanisms.Comment: Extended abstract appeared in Proc. of 16th ACM Conference on
Economics and Computation (EC'15
User equilibrium traffic network assignment with stochastic travel times and late arrival penalty
The classical Wardrop user equilibrium (UE) assignment model assumes traveller choices are based on fixed, known travel times, yet these times are known to be rather variable between trips, both within and between days; typically, then, only mean travel times are represented. Classical stochastic user equilibrium (SUE) methods allow the mean travel times to be differentially perceived across the population, yet in a conventional application neither the UE or SUE approach recognises the travel times to be inherently variable. That is to say, there is no recognition that drivers risk arriving late at their destinations, and that this risk may vary across different paths of the network and according to the arrival time flexibility of the traveller. Recent work on incorporating risky elements into the choice process is seen either to neglect the link to the arrival constraints of the traveller, or to apply only to restricted problems with parallel alternatives and inflexible travel time distributions. In the paper, an alternative approach is described based on the âschedule delayâ paradigm, penalising late arrival under fixed departure times. The approach allows flexible travel time densities, which can be fitted to actual surveillance data, to be incorporated. A generalised formulation of UE is proposed, termed a Late Arrival Penalised UE (LAPUE). Conditions for the existence and uniqueness of LAPUE solutions are considered, as well as methods for their computation. Two specific travel time models are then considered, one based on multivariate Normal arc travel times, and an extended model to represent arc incidents, based on mixture distributions of multivariate Normals. Several illustrative examples are used to examine the sensitivity of LAPUE solutions to various input parameters, and in particular its comparison with UE predictions. Finally, paths for further research are discussed, including the extension of the model to include elements such as distributed arrival time constraints and penalties
Well Structured Transition Systems with History
We propose a formal model of concurrent systems in which the history of a
computation is explicitly represented as a collection of events that provide a
view of a sequence of configurations. In our model events generated by
transitions become part of the system configurations leading to operational
semantics with historical data. This model allows us to formalize what is
usually done in symbolic verification algorithms. Indeed, search algorithms
often use meta-information, e.g., names of fired transitions, selected
processes, etc., to reconstruct (error) traces from symbolic state exploration.
The other interesting point of the proposed model is related to a possible new
application of the theory of well-structured transition systems (wsts). In our
setting wsts theory can be applied to formally extend the class of properties
that can be verified using coverability to take into consideration (ordered and
unordered) historical data. This can be done by using different types of
representation of collections of events and by combining them with wsts by
using closure properties of well-quasi orderings.Comment: In Proceedings GandALF 2015, arXiv:1509.0685
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