构建在联接探索和分解分布上分布估计算法的扩展算法

遗传算法（GA）在解决变量间存在较大相互作用优化问题时缺乏有效性，一种解决问题的途径是分布估计算法（EDA）。分解分布算法是一种近似高阶相互作用的EDA，它用分解Boltzmann分布来产生新的解。运用联接探测及分解分布给出一个以高概率找到最优解的新算法。该算法能解决一些分布估计算法难于处理的问题。实验证明了算法的可行性和有效性。     

一种信任关系强度敏感的社会化推荐算法

为了进一步提高推荐算法的准确率,更好地对用户间的信任关系进行建模,首先提出了一种信任关系强度敏感的社会化推荐算法(StrengthMF).与以往的算法相比,该算法假设建立信任关系的两个用户之间并不一定存在着相似的兴趣爱好.在推荐过程中,StrengthMF算法通过共享的潜在用户特征空间来对信任关系强度和用户兴趣进行建模,通过进一步识别出那些与目标用户有着共同爱好的朋友来对求解的过程进行优化.为了验证算法所估计出的信任关系强度的准确性,接着又在SocialMF算法的基础上,提出了一种使用所估计的信任关系对其重新训练和学习的InfluenceMF算法.实验结果表明,与目前较为流行的方法相比,新方法能在RMSE和MAE上取得更好的推荐结果,其所推导出的信任关系强度能进一步提高已有推荐算法的性能.     

利用三次剩余构造的基于身份环签名方案

传统的基于身份环签名方案大多采用双线性配对实现,但配对方法的运算复杂度较高,会大幅降低签名方案的效率。为此,提出一种非配对的环签名方案。给出用于有效计算三次剩余3′次根的算法,在该算法的基础上生成签名密钥,并结合三次剩余理论构造基于身份的环签名方案。分析结果表明,在大整数分解困难问题的假设前提下,该方案在随机预言模型下被证明是选择消息和身份安全的。同时,该方案也满足签名者无条件匿名性。     

Filtering,smoothing and prediction using a control‐loop spectral factorization method for coloured noise

A method for the linear least‐squares estimation of random signals contaminated with random noise that uses a new method of spectral factorization is shown. It is shown that the optimal filter can be written entirely in terms of the two spectral factors of signal plus noise and noise‐alone, and can be applied to the general case of coloured and white additive noise. The method of spectral factorization used is novel and uses control‐system methodology. Copyright © 2012 John Wiley & Sons, Ltd.     

Computing Longest Previous Factor in linear time and applications

We give two optimal linear-time algorithms for computing the Longest Previous Factor (LPF) array corresponding to a string w. For any position i in w, LPF[i] gives the length of the longest factor of w starting at position i that occurs previously in w. Several properties and applications of LPF are investigated. They include computing the Lempel-Ziv factorization of a string and detecting all repetitions (runs) in a string in linear time independently of the integer alphabet size.     

Identification of certain noisy MA models: new results

In this paper, we address the identification problem of p-inputs q-outputs MA models, corrupted by a white noise with unknown covariance matrix, in the case where p<q. Under certain additional conditions, we show that the generating function of the MA model is identifiable (up to a p×p constant orthogonal matrix) from the autocovariance function of the observation. Our results extend those already obtained in Desbouvries et al. [5] and Desbouvries and Loubaton [6].     

Linear Operator Inequalities for Strongly Stable Weakly Regular Linear Systems

We consider the question of the existence of solutions to certain linear operator inequalities (Lur'e equations) for strongly stable, weakly regular linear systems with generating operators A, B, C, 0. These operator inequalities are related to the spectral factorization of an associated Popov function and to singular optimal control problems with a nonnegative definite quadratic cost functional. We split our problem into two subproblems: the existence of spectral factors of the nonnegative Popov function and the existence of a certain extended output map. Sufficient conditions for the solvability of the first problem are known and for the case that A has compact resolvent and its eigenvectors form a Riesz basis for the state space, we give an explicit solution to the second problem in terms of A, B, C and the spectral factor. The applicability of these results is demonstrated by various heat equation examples satisfying a positive-real condition. If (A, B) is approximately controllable, we obtain an alternative criterion for the existence of an extended output operator which is applicable to retarded systems. The above results have been used to design adaptive observers for positive-real infinite-dimensional systems. Date received: July 25, 1997. Date revised: February 10, 2001.     

Necessary and sufficient conditions on Q ( = C( I + PC)) for stabilization of the linear feedback system S(P, C)

We find the following necessary and sufficient conditions for Q (:=C(I+PC)⁻¹) to -stabilize the standard linear time-invariant unity feedback system S(P, C) where P has the l.c.f. (D_pl, N_pl) and the r.c.f. (N_pr, D_pr); and is a principal ideal domain. (i) Q must have elements in (ii) (resp. (iii)) Q must factorize in with D_pr, (resp. D_pl) as a left (resp. right) factor and (iv) (I – QP) must factor in with D_pr, as a left factor.     

Collaborative filtering with interlaced generalized linear models

Collaborative filtering (CF) is a data analysis task appearing in many challenging applications, in particular data mining in Internet and e-commerce. CF can often be formulated as identifying patterns in a large and mostly empty rating matrix. In this paper, we focus on predicting unobserved ratings. This task is often a part of a recommendation procedure. We propose a new CF approach called interlaced generalized linear models (GLM); it is based on a factorization of the rating matrix and uses probabilistic modeling to represent uncertainty in the ratings. The advantage of this approach is that different configurations, encoding different intuitions about the rating process can easily be tested while keeping the same learning procedure. The GLM formulation is the keystone to derive an efficient learning procedure, applicable to large datasets. We illustrate the technique on three public domain datasets.     

Expression for massively parallel algorithms — description and illustrative example

In this paper, we present a way of expressing massively parallel algorithms associated with a programming environment for networks of processors with local memory; the aim is to provide a programming method that is independent of the network topology, and that leads to an efficient implementation for communications.

Then, we illustrate these concepts using as an example a scientific algorithm: Choleski's method for a parallel factorization of sparse matrices into blocks.

This work gives rise to software for a T.Node multiprocessor computer.