基于机制转换模型的碳排放权期权定价

机制转换模型可以将外部环境的变化迅速反映到对模型参数的调整中,故运用马氏链刻画外部机制建立机制转换模型,基于此进行碳排放权期权定价。为实现其价值函数的数值计算,首次设计并证明了一套倒向递归算法,该算法依据马氏链跳跃的划分实现递归,从而克服了马氏链带来的运算高复杂度,其数值结果展示了完整的波动率微笑和期限结构。最后通过与前人提出的算法以及蒙特卡洛模拟比较表明,倒向递归算法可获得更高的准确性和运算效率。     

全错位排列问题的基于芯片的DNA计算模型

全错位排列问题作为组合数学中一个重要的问题,到目前为止还没有好的算法.应用DNA芯片技术,提出了全错位排列问题的基于芯片的DNA计算模型,并对模型进行了简要分析.     

椭圆曲线密码的快速算法

80年代,椭圆曲线理论被引入数据加密领域,形成了一种新的公开密钥体制即椭圆曲线密码体制(ECC).该体制中,最耗时的运算是倍点运算也就是椭圆曲线上的点与一个整数的乘法运算.因此倍点运算的快速计算是椭圆曲线密码快速实现的关键.本文提出一种计算kP新的算法,使效率提高38%以上.     

锥束FDK反投影重建算法的改进

FDK算法本质上是将二维的FBP算法推广到三维空间,其基本步骤可归纳为三步,分别是加权,滤波和反投影重建.在加权反投影重建的过程中需要重复计算相关函数,这会导致算法运算效率降低,考虑到相关函数的计算具有周期性,因此只需计算其中一部分,其余部分用已经算出的部分进行表示即可.同时,在加权反投影重建时需要进行二维插值,综合利用最近邻插值法和双线性插值法.通过建立仿真模型,分组实验得出改进后的FDK算法运算效率更高,重建图像更加清晰.     

仅运用一个特征向量计算可对角化转移矩阵的吸收马尔可夫链的平稳分布（英文）

吸收马尔可夫链是一种重要的统计模型,广泛地被用于众多学科中的算法建模,如在数字图像处理、网络分析等中.为了得到该模型的平稳分布,通常需要计算转移矩阵的逆,但是对于大型矩阵来说这仍然是比较困难且耗费计算量的.在本文中,对于含有两个吸收态的马尔可夫链,当其转移矩阵可对角化时,我们提出了一种简单方法来计算其平稳分布.在该方法中仅仅需要计算特征值1对应的一个特征向量即可.我们运用该方法,从矩阵的角度推导出了赌徒破产问题的相关概率.同时本方法也能够处理该问题的复杂扩展形式.事实上,本方案是对处理吸收马尔可夫链的通用方法的一个变种,因此在通用方法中也能够采用类似的技术来避免矩阵求逆运算.     

具有机器适用限制的分布式置换流水车间问题的模型与算法

考虑具有机器适用限制的多个不同置换流水车间的调度问题. 机器适用限制指的是每个工件只能分配到其可加工工厂集合. 所有置换流水车间拥有的机器数相同但是具有不同的加工能力. 首先, 针对该问题建立了基于位置的混合整数线性规划模型; 进而, 对一般情况和三种特殊情况给出了具有较小近似比的多项式时间算法. 其次, 基于NEH方法提出了启发式算法NEHg, 并给出了以NEHg为上界的分支定界算法. 最后, 通过例子说明了NEHg启发式算法和分支定界算法的计算过程, 并进行大量的实验将NEHg与NEH算法结果进行比较, 从而验证了NEHg算法的有效性.     

基于贝叶斯统计方法的两总体基因表达数据分类

在疾病的诊断过程中,对疾病的精确分类是提高诊断准确率和疾病治愈率至 关重要的一个环节,DNA芯片技术的出现使得我们从微观的层次获得与疾病分类及诊断 密切相关的基因功能信息．但是DNA芯片技术得到的基因的表达模式数据具有多变量小 样本特点,使得分类过程极不稳定,因此我们首先筛选出表达模式发生显著性变化的基因 作为特征基因集合以减少变量个数,然后再根据此特征基因集合建立分类器对样本进行分 类．本文运用似然比检验筛选出特征基因,然后基于贝叶斯方法建立了统计分类模型,并 应用马尔科夫链蒙特卡罗(MCMC)抽样方法计算样本归类后验概率．最后我们将此模型 应用到两组真实的DNA芯片数据上,并将样本成功分类．     

基于移动Agent技术的网格负载均衡调度方法

为了实现网格计算资源的动态自适应性管理和负载均衡调度,将移动Agent技术引入网格资源管理,并提出了一种基于Agent的网格资源调度模型.在该模型基础上,采用遗传克隆算法解决网格计算中的任务调度问题.仿真实验表明该算法不仅充分发挥了Agent的智能性、自主性,还具有良好的扩展性,提高了网格资源调度的效率.     

基于盲数理论和随机模拟的河流水环境容量计算

针对水环境系统的不确定性,将延拓盲数引入到水环境容量计算模型中,并运用随机模拟方法对盲参数进行模拟,把盲数之间的运算转化为普通实数之间的运算,从而建立了水环境容量的延拓盲数与随机模拟的耦合模型.经实例研究,得到研究对象的水环境容量随机模拟分布以及在不同保证率下的水环境容量值,为研究对象当地的水环境规划和保护提供丰富可靠的依据.     

第六讲 扫除算法

1.引言 扫除算子(Sweep operator)是对矩阵的一种变换运算,也称为扫除变换或扫除算法.其实质是高斯──约唐消去法求逆矩阵的一种改进算法. 扫除算法可用于求解线代数方程组,计算矩阵的逆阵(包括广义道),也可以用于计算行列式的值.在统计计算中,扫除算法有很丰富的统计含义,它是回归分析、判别分析及各种逐步算法的基础.本文将从矩阵代数运算和统计含义两个方面对扫除算法作一个简要的介绍.最后还给出FORTRAN程序. 2.从回归计算谈起 设线性回归模型为Y=Xβ+e　　　(2.1)其中 X,Y可为观测数据,β为回归系数,e为随机误差.通常假设有m个自…     

Linear relations in time series models. II

In this paper an asymptotic theory is developed for a new time series model which was introduced in a previous paper [5]. An algorithm for computing estimates of the parameters of this time series model is given, and it is shown that these estimators are asymptotically efficient in the sense that they have the same asymptotic distribution as the maximum likelihood estimators.     

Engineering graph-based models for dynamic timetable information systems

In the last years we have witnessed remarkable progress in providing efficient algorithmic solutions to the problem of computing best journeys (or routes) in schedule-based public transportation systems. We have now models to represent timetables that allow us to answer queries for optimal journeys in a few milliseconds, also at a very large scale. Such models can be classified into two types: those representing the timetable as an array, and those representing it as a graph. Array-based models have been shown to be very effective in terms of query time, while graph-based ones usually answer queries by computing shortest paths, and hence they are suitable to be combined with the speed-up techniques developed for road networks.In this paper, we study the behavior of graph-based models in the prominent case of dynamic scenarios, i.e., when delays might occur to the original timetable. In particular, we make the following contributions. First, we consider the graph-based reduced time-expanded model and give a simplified and optimized routine for handling delays, and a re-engineered and fine-tuned query algorithm. Second, we propose a new graph-based model, namely the dynamic timetable model, natively tailored to efficiently incorporate dynamic updates, along with a query algorithm and a routine for handling delays. Third, we show how to adapt the ALT algorithm to such graph-based models. We have chosen this speed-up technique since it supports dynamic changes, and a careful implementation of it can significantly boost its performance. Finally, we provide an experimental study to assess the effectiveness of all proposed models and algorithms, and to compare them with the array-based state of the art solution for the dynamic case. We evaluate both new and existing approaches by implementing and testing them on real-world timetables subject to synthetic delays.Our experimental results show that: (i) the dynamic timetable model is the best model for handling delays; (ii) graph-based models are competitive to array-based models with respect to query time in the dynamic case; (iii) the dynamic timetable model compares favorably with both the original and the reduced time-expanded model regarding space; (iv) combining the graph-based models with speed-up techniques designed for road networks, such as ALT, is a very promising approach.     

Blockwise acceleration of alternating least squares for canonical tensor decomposition

The canonical polyadic (CP) decomposition of tensors is one of the most important tensor decompositions. While the well-known alternating least squares (ALS) algorithm is often considered the workhorse algorithm for computing the CP decomposition, it is known to suffer from slow convergence in many cases and various algorithms have been proposed to accelerate it. In this article, we propose a new accelerated ALS algorithm that accelerates ALS in a blockwise manner using a simple momentum-based extrapolation technique and a random perturbation technique. Specifically, our algorithm updates one factor matrix (i.e., block) at a time, as in ALS, with each update consisting of a minimization step that directly reduces the reconstruction error, an extrapolation step that moves the factor matrix along the previous update direction, and a random perturbation step for breaking convergence bottlenecks. Our extrapolation strategy takes a simpler form than the state-of-the-art extrapolation strategies and is easier to implement. Our algorithm has negligible computational overheads relative to ALS and is simple to apply. Empirically, our proposed algorithm shows strong performance as compared to the state-of-the-art acceleration techniques on both simulated and real tensors.     

High‐performance direct algorithms for computing the sign function of triangular matrices

Algorithms and implementations for computing the sign function of a triangular matrix are fundamental building blocks for computing the sign of arbitrary square real or complex matrices. We present novel recursive and cache‐efficient algorithms that are based on Higham's stabilized specialization of Parlett's substitution algorithm for computing the sign of a triangular matrix. We show that the new recursive algorithms are asymptotically optimal in terms of the number of cache misses that they generate. One algorithm that we present performs more arithmetic than the nonrecursive version, but this allows it to benefit from calling highly optimized matrix multiplication routines; the other performs the same number of operations as the nonrecursive version, suing custom computational kernels instead. We present implementations of both, as well as a cache‐efficient implementation of a block version of Parlett's algorithm. Our experiments demonstrate that the blocked and recursive versions are much faster than the previous algorithms and that the inertia strongly influences their relative performance, as predicted by our analysis.     

Strengthening hash families and compressive sensing

The deterministic construction of measurement matrices for compressive sensing is a challenging problem, for which a number of combinatorial techniques have been developed. One of them employs a widely used column replacement technique based on hash families. It is effective at producing larger measurement matrices from smaller ones, but it can only preserve the strength (level of sparsity supported), not increase it. Column replacement is extended here to produce measurement matrices with larger strength from ingredient arrays with smaller strength. To do this, a new type of hash family, called a strengthening hash family, is introduced. Using these hash families, column replacement is shown to increase strength under two standard notions of recoverability. Then techniques to construct strengthening hash families, both probabilistically and deterministically, are developed. Using a variant of the Stein–Lovász–Johnson theorem, a deterministic, polynomial time algorithm for constructing a strengthening hash family of fixed strength is derived.     

基于EM算法的混合模型中子总体个数的研究

混合模型已成为数据分析中最流行的技术之一,由于拥有数学模型,它通常比聚类分析中的传统的方法产生的结果更精确,而关键因素是混合模型中子总体个数,它决定了数据分析的最终结果。期望最大化(EM)算法常用在混合模型的参数估计,以及机器学习和聚类领域中的参数估计中,是一种从不完全数据或者是有缺失值的数据中求解参数极大似然估计的迭代算法。学者们往往采用AIC和BIC的方法来确定子总体的个数,而这两种方法在实际的应用中的效果并不稳定,甚至可能会产生错误的结果。针对此问题,本文提出了一种利用似然函数的碎石图来确定混合模型中子总体的个数的新方法。实验结果表明,本文方法确定的子总体的个数在大部分理想的情况下可以得到与AIC、BIC方法确定的聚类个数相同的结果,而在一般的实际数据中或条件不理想的状态下,碎石图方法也可以得到更可靠的结果。随后,本文将新方法在选取的黄石公园喷泉数据的参数估计中进行了实际的应用。     

计算Hamilton矩阵特征值的一个稳定的有效的保结构的算法

提出了一个稳定的有效的保结构的计算Hamilton矩阵特征值和特征不变子空间的算法,该算法是由SR算法改进变形而得到的。在该算法中,提出了两个策略,一个叫做消失稳策略,另一个称为预处理技术。在消失稳策略中,通过求解减比方程和回溯彻底克服了Bunser Gerstner和Mehrmann提出的SR算法的严重失稳和中断现象的发生,两种策略的实施的代价都非常低。数值算例展示了该算法比其它求解Hamilton矩阵特征问题的算法更有效和可靠。     

Solving Stochastic Linear Programs with Restricted Recourse Using Interior Point Methods

In this paper we present a specialized matrix factorization procedure for computing the dual step in a primal-dual path-following interior point algorithm for solving two-stage stochastic linear programs with restricted recourse. The algorithm, based on the Birge-Qi factorization technique, takes advantage of both the dual block-angular structure of the constraint matrix and of the special structure of the second-stage matrices involved in the model. Extensive computational experiments on a set of test problems have been conducted in order to evaluate the performance of the developed code. The results are very promising, showing that the code is competitive with state-of-the-art optimizers.     

Computational results of an interior point algorithm for large scale linear programming

This paper gives computational results for an efficient implementation of a variant of dual projective algorithm for linear programming. The implementation uses the preconditioned conjugate gradient method for computing projections. Our computational experience reported in this paper indicates that this algorithm has potential as an alternative for solving very large LPs in which the direct methods fail due to memory and CPU time requirements. The conjugate gradient algorithm was able to find very accurate directions even when the system was ill-conditioned. The paper also discusses a new mathematical technique called the reciprocal estimates for estimating the primal variables. We have conducted extensive computational experiments on problems representative of large classes of applications of current interest. We have also chosen instances of the problems of future potential interest, which could not be solved in the past due to the weakness of the prior solution methods, but which represent a large class of new applications. The hypergraph model is such an example. Comparison of our implementation with MINOS 5.1 shows that our implementation is orders of magnitude faster than MINOS 5.1 for these problems.     

Innovation algorithm in ARMA process

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jenkins(1976). If the data exhibits no apparent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocorrelation function as suggested by Box and Jenkins (1976). Many new techniques have emerged in the literature and it is found that most of them are of very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis, 1987) are used as recursive methods for computing best linear predictors in an ARMA(p,q) model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to any real world data.