282,610 research outputs found
Monte Carlo methods in PageRank computation: When one iteration is sufficient
PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as a frequency of visiting a Web page by a random surfer and thus it reflects the popularity of a Web page. Google computes the PageRank using the power iteration method which requires about one week of intensive computations. In the present work we propose and analyze Monte Carlo type methods for the PageRank computation. There are several advantages of the probabilistic Monte Carlo methods over the deterministic power iteration method: Monte Carlo methods provide good estimation of the PageRank for relatively important pages already after one iteration; Monte Carlo methods have natural parallel implementation; and finally, Monte Carlo methods allow to perform continuous update of the PageRank as the structure of the Web changes
Computational aspects of helicopter trim analysis and damping levels from Floquet theory
Helicopter trim settings of periodic initial state and control inputs are investigated for convergence of Newton iteration in computing the settings sequentially and in parallel. The trim analysis uses a shooting method and a weak version of two temporal finite element methods with displacement formulation and with mixed formulation of displacements and momenta. These three methods broadly represent two main approaches of trim analysis: adaptation of initial-value and finite element boundary-value codes to periodic boundary conditions, particularly for unstable and marginally stable systems. In each method, both the sequential and in-parallel schemes are used and the resulting nonlinear algebraic equations are solved by damped Newton iteration with an optimally selected damping parameter. The impact of damped Newton iteration, including earlier-observed divergence problems in trim analysis, is demonstrated by the maximum condition number of the Jacobian matrices of the iterative scheme and by virtual elimination of divergence. The advantages of the in-parallel scheme over the conventional sequential scheme are also demonstrated
Explicit symmetric Runge-Kutta-Nyström methods for parallel computers
AbstractIn this paper, we are concerned with parallel predictor-corrector (PC) iteration of Runge-Kutta-Nyström (RKN) methods in P(EC)mE mode for integrating initial value problems for the special second-order equation y″(t) = f(y(t)). We consider symmetric Runge-Kutta-Nyström (SRKN) corrector methods based on direct collocation techniques which optimize the rate of convergence of the PC iteration process. The resulting PISRKN methods (parallel iterated SRKN methods) are shown to be much more efficient when they are compared to the PC iteration process applied to the Gauss-Legendre RKN correctors
Convergence Rates with Inexact Non-expansive Operators
In this paper, we present a convergence rate analysis for the inexact
Krasnosel'skii-Mann iteration built from nonexpansive operators. Our results
include two main parts: we first establish global pointwise and ergodic
iteration-complexity bounds, and then, under a metric subregularity assumption,
we establish local linear convergence for the distance of the iterates to the
set of fixed points. The obtained iteration-complexity result can be applied to
analyze the convergence rate of various monotone operator splitting methods in
the literature, including the Forward-Backward, the Generalized
Forward-Backward, Douglas-Rachford, alternating direction method of multipliers
(ADMM) and Primal-Dual splitting methods. For these methods, we also develop
easily verifiable termination criteria for finding an approximate solution,
which can be seen as a generalization of the termination criterion for the
classical gradient descent method. We finally develop a parallel analysis for
the non-stationary Krasnosel'skii-Mann iteration. The usefulness of our results
is illustrated by applying them to a large class of structured monotone
inclusion and convex optimization problems. Experiments on some large scale
inverse problems in signal and image processing problems are shown.Comment: This is an extended version of the work presented in
http://arxiv.org/abs/1310.6636, and is accepted by the Mathematical
Programmin
Parallel Self-Consistent-Field Calculations via Chebyshev-Filtered Subspace Acceleration
Solving the Kohn-Sham eigenvalue problem constitutes the most computationally
expensive part in self-consistent density functional theory (DFT) calculations.
In a previous paper, we have proposed a nonlinear Chebyshev-filtered subspace
iteration method, which avoids computing explicit eigenvectors except at the
first SCF iteration. The method may be viewed as an approach to solve the
original nonlinear Kohn-Sham equation by a nonlinear subspace iteration
technique, without emphasizing the intermediate linearized Kohn-Sham eigenvalue
problem. It reaches self-consistency within a similar number of SCF iterations
as eigensolver-based approaches. However, replacing the standard
diagonalization at each SCF iteration by a Chebyshev subspace filtering step
results in a significant speedup over methods based on standard
diagonalization. Here, we discuss an approach for implementing this method in
multi-processor, parallel environment. Numerical results are presented to show
that the method enables to perform a class of highly challenging DFT
calculations that were not feasible before
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