Randomization helps to perform independent tasks reliably

This paper is about algorithms that schedule tasks to be performed in a distributed failure‐prone environment, when processors communicate by message‐passing, and when tasks are independent and of unit length. The processors work under synchrony and may fail by crashing. Failure patterns are imposed by adversaries. Linearly‐bounded adversaries may fail up to a constant fraction of the processors. Weakly‐adaptive adversaries have to select, prior to the start of an execution, a subset of processors to be failure‐prone, and then may fail only the selected processors, at arbitrary steps, in the course of the execution. Strongly adaptive adversaries have a total number of failures as the only restriction on failure patterns. The measures of complexity are work, measured as the available processor steps, and communication, measured as the number of point‐to‐point messages. A randomized algorithm is developed, that attains both ??(n log*n) expected work and ??(n log*n) expected communication, against weakly‐adaptive linearly‐bounded adversaries, in the case when the numbers of tasks and processors are both equal to n. This is in contrast with performance of algorithms against strongly‐adaptive linearly‐bounded adversaries, which has to be Ω(n log n/log log n) in terms of work. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 2004     

Fabrication and transport properties of multiwall carbon nanotube crossroads

A multiwall carbon nanotube crossroads has been fabricated by a manipulation technique using a glass microcapillary, and the low temperature transport properties investigated. The two-terminal conductance of an individual tube shows Tomonaga–Luttinger liquid behavior G∝T^α at high temperature and dI/dV ∝V ^α at low temperature. However, no evidence of such a power-law behavior is obtained in the four-terminal conductance at the junction, where the conductance shows an almost metallic behavior ‘corrected’ by weak localization. Weak localization would essentially appear in electron states at the junctions of MWNTs.     

Addendum and Corrigendum to "Local Saturation of Conservative Operators'

In this note we make a correction in the paper [1] by two of the authors. Specifically we modify an auxiliary localization result and prove that the applications to the saturation theory of some outstanding conservative operators remain valid. Moreover, we enrich a corollary dealing with the well-known Szász-Mirakjan operators. This revised version was published online in June 2006 with corrections to the Cover Date.     

Approximation bounds for quadratic maximization and max-cut problems with semidefinite programming relaxation

In this paper,we consider a class of quadratic maximization problems.For a subclass of the problems,we show that the SDP relaxation approach yields an approximation solution with the ratio is dependent on the data of the problem with α being a uniform lower bound.In light of this new bound,we show that the actual worst-case performance ratio of the SDP relaxation approach (with the triangle inequalities added) is at least α δd if every weight is strictly positive,where δd ＞ 0 is a constant depending on the problem dimension and data.     

Fitting the control parameters of a genetic algorithm: An application to technical trading systems design

This paper studies the problem of how changes in the design of the genetic algorithm (GA) have an effect on the results obtained in real-life applications. In this study, focused on the application of a GA to the tuning of technical trading rules in the context of financial markets, our tentative thesis is that the GA is robust with respect to design changes. The optimization of technical trading systems is a suitable area for the application of the GA metaheuristic, as the complexity of the problem grows exponentially as new technical rules are added to the system and as the answer time is crucial when applying the system to real-time data. Up to now, most of GAs applications to this subject obviated the question of possible “design dependence” in their results. The data we report, based on our experiments, do not allow us to refute the hypothesis of robustness of the GA to design implementation, when applying to technical trading systems tuning.     

光传送网中结合传输损伤的路由和波长分配

在波长路由光网优化设计的过程中考虑了物理传输损伤造成的限制,提出了一种联合优化思路.将光信号在传输过程中的物理损伤作为问题公式的另一维约束,给出了相应的启发式算法,并按照由粗到细的机制对所提出的思路做了相应的评估,同时对负荷平衡问题作了初步的分析.数值结果表明这种算法可以获得与优化值相近的波长需求解,并且能够保证网络的传输性能,其业务分布随着备选路径的增加而趋向均衡.     

A parallel version of the preconditioned conjugate gradient method for boundary element equations

The parallel version of precondition techniques is developed for matrices arising from the Galerkin boundary element method for two-dimensional domains with Dirichlet boundary conditions. Results were obtained for implementations on a transputer network as well as on an nCUBE-2 parallel computer showing that iterative solution methods are very well suited for a MIMD computer. A comparison of numerical results for iterative and direct solution methods is presented and underlines the superiority of iterative methods for large systems.     

Analysis and implementation of cross sync period underwater acoustical positioning techniques for underwater targets

I.IlltroductionWhenanactiveunderwateracousticalpositioningsystemissearchingforanunderwatertarget,itisnecessaryforittocompleteatransmittingandreceivingcycIewithineachworkingperiodinordertofindoutthelocationofthetargetcorrectly.Forexample,whenanunder-wateracoustica1synchronouspositioningsystemistrackinganunderwatertargetwithagivensynchr0nizingperiod,thepropagati0ntimeofthepositioningsignaltravellingfr0mthetaJrgettothearrayofthesystemshouldbelessthanthesynchronizingperiod0therwisetargetdis-tance…     

几种约束广义逆矩阵的有限算法

1引言与引理众所周知，关于非奇异方阵的正则逆的有限算法是由Faddeev大给在1949年之前提出的，这就是著名的Faddeev算法[1，P…334-336]。自从五十年代中期广义逆矩阵的研究复兴与发展以来，有不少学者提出了关于广义逆矩阵的有限算法。第一个给出关于广义逆矩     

Primal-dual proximal point algorithm for linearly constrained convex programming problems

A primal-dual version of the proximal point algorithm is developed for linearly constrained convex programming problems. The algorithm is an iterative method to find a saddle point of the Lagrangian of the problem. At each iteration of the algorithm, we compute an approximate saddle point of the Lagrangian function augmented by quadratic proximal terms of both primal and dual variables. Specifically, we first minimize the function with respect to the primal variables and then approximately maximize the resulting function of the dual variables. The merit of this approach exists in the fact that the latter function is differentiable and the maximization of this function is subject to no constraints. We discuss convergence properties of the algorithm and report some numerical results for network flow problems with separable quadratic costs.