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精确制导的导弹的 CEP=8m，以高低角度为 45 度，方位角为 0 度的姿态摧毁目标，且引信 定高 5m，服从均匀分布[-1 1]之间的均匀分布。 （注：只需考虑末端以固定的姿态角，弹道呈 与制导平面垂直的直

这是一本关于随机规划比较全面的书！比较难，不太容易啃，但是读了之后收获很大。这是高清版的！To Julia, Benjamin, Daniel, Nalan, and Yael;to Tsonka Konstatin and Marekand to the memory of feliks, Maria, and dentcho2009/8/20pagContentsList of notationserace1 Stochastic Programming ModelsIntroduction1.2 Invento1.2.1The news vendor problem1.2.2Constraints12.3Multistage modelsMultiproduct assembl1.3.1Two-Stage Model1.3.2Chance Constrained ModeMultistage modelPortfolio selection131.4.1Static model14.2Multistage Portfolio selection14.3Decision rule211.5 Supply Chain Network Design22Exercises2 Two-Stage Problems272.1 Linear Two-Stage Problems2.1.1Basic pi272.1.2The Expected Recourse Cost for Discrete Distributions 302.1.3The Expected Recourse Cost for General Distributions.. 322.1.4Optimality Conditions垂Polyhedral Two-Stage Problems422.2.1General Properties422.2.2Expected recourse CostOptimality conditions2.3 General Two-Stage Problems82.3.1Problem Formulation, Interchangeability482.3.2Convex Two-Stage Problems2.4 Nonanticipativity2009/8/20page villContents2.4.1Scenario formulation2.4.2Dualization of Nonanticipativity Constraints2.4.3Nonanticipativity duality for general Distributions2.4.4Value of perfect infExercises3 Multistage problems3. 1 Problem Formulation633.1.1The general setting3.1The Linear case653.1.3Scenario trees3.1.4Algebraic Formulation of nonanticipativity constraints 7lDuality....763.2.1Convex multistage problems·763.2.2Optimality Conditions3.2.3Dualization of Feasibility Constraints3.2.4Dualization of nonanticipativity ConstraintsExercises4 Optimization models with Probabilistic Constraints874.1 Introduction874.2 Convexity in Probabilistic Optimization4.2Generalized Concavity of Functions and measures4.2.2Convexity of probabilistically constrained sets1064.2.3Connectedness of Probabilistically Constrained Sets... 113Separable probabilistic Constraints.1144.3Continuity and Differentiability Properties ofDistribution functions4.3.2p-Efficient Points.1154.3.3Optimality Conditions and Duality Theory1224 Optimization Problems with Nonseparable Probabilistic Constraints.. 1324.4Differentiability of Probability Functions and OptimalityConditions13344.2Approximations of Nonseparable ProbabilisticConstraints134.5 Semi-infinite Probabilistic Problems144E1505 Statistical Inference155Statistical Properties of Sample Average Approximation Estimators.. 1555.1.1Consistency of SAA estimators1575.1.2Asymptotics of the saa Optimal value1635.1.3Second order asStochastic Programs5.2 Stoch1745.2.1Consistency of solutions of the SAA GeneralizedEquatio1752009/8/20pContents5.2.2Atotics of saa generalized equations estimators 1775.3 Monte Carlo Sampling Methods180Exponential Rates of Convergence and Sample sizeEstimates in the Case of a finite Feasible se1815.3.2Sample size estimates in the General Case1855.3.3Finite Exponential Convergence1915.4 Quasi-Monte Carlo Methods1935.Variance-Reduction Techniques198Latin hmpling1985.5.2Linear Control random variables method200ng and likelihood ratio methods 205.6 Validation analysis5.6.1Estimation of the optimality g2025.6.2Statistical Testing of Optimality Conditions2075.7Constrained Probler5.7.1Monte Carlo Sampling Approach2105.7.2Validation of an Optimal solution5.8 SAA Method Applied to Multistage Stochastic Programmin205.8.1Statistical Properties of Multistage SAA Estimators22l5.8.2Complexity estimates of Multistage Programs2265.9 Stochastic Approximation Method2305.9Classical Approach5.9.2Robust sA approach..23359.3Mirror Descent sa method235.9.4Accuracy Certificates for Mirror Descent Sa Solutions.. 244Exercis6 Risk Averse Optimi2536.1 Introductio6.2 Mean-Risk models.2546.2.1Main ideas of mean -Risk analysis546.2.2Semideviation6.2.3Weighted Mean Deviations from Quantiles.2566.2.4Average value-at-Risk2576.3 Coherent risk measures2616.3.1Differentiability Properties of Risk Measures2656.3.2Examples of risk Measures..2696.3.3Law invariant risk measures and Stochastic orders2796.3.4Relation to Ambiguous Chance Constraints2856.4 Optimization of risk measures.2886.4.1Dualization of Nonanticipativity Constraints2916.4.2Examples...2956.5 Statistical Properties of Risk measures6.5.IAverage value-at-Ris6.52Absolute semideviation risk measure301Von mises statistical functionals3046.6The problem of moments306中2009/8/20page xContents6.7 Multistage Risk Averse Optimization3086.7.1Scenario tree formulation3086.7.2Conditional risk mappings3156.7.3Risk Averse multistage Stochastic Programming318Exercises3287 Background material3337.1 Optimization and Convex Analysis..334Directional Differentiability3347.1.2Elements of Convex Analysis3367.1.3Optimization and duality3397.1.4Optimality Conditions.............3467.1.5Perturbation analysis3517.1.6Epiconvergence3572 Probability3597.2.1Probability spaces and random variables7.2.2Conditional Probability and Conditional Expectation... 36372.3Measurable multifunctions and random functions3657.2.4Expectation Functions.3687.2.5Uniform Laws of Large Numbers...,,3747.2.6Law of Large Numbers for Random Sets andSubdifferentials3797.2.7Delta method7.2.8Exponential Bounds of the Large Deviations Theory3877.2.9Uniform Exponential Bounds7.3 Elements of Functional analysis3997.3Conjugate duality and differentiability.......... 4017.3.2Lattice structure4034058 Bibliographical remarks407Biibliography415Index4312009/8/20pageList of Notationsequal by definition, 333IR", n-dimensional space, 333A, transpose of matrix(vector)A, 3336I, domain of the conjugate of risk mea-C(X) space of continuous functions, 165sure p, 262CK, polar of cone C, 337Cn, the space of nonempty compact sub-C(v,R"), space of continuously differ-sets of r 379entiable mappings,176set of probability density functions,I Fr influence function. 3042L, orthogonal of (linear) space L, 41Sz, set of contact points, 3990(1), generic constant, 188b(k; a, N), cdf of binomial distribution,Op(), term, 382214S, the set of &-optimal solutions of theo, distance generating function, 236true problem, 18g(x), right-hand-side derivative, 297Va(a), Lebesgue measure of set A C RdCl(A), topological closure of set A, 334195conv(C), convex hull of set C, 337W,(U), space of Lipschitz continuousCorr(X, Y), correlation of X and Y 200functions. 166. 353CoV(X, Y, covariance of X and y, 180[a]+=max{a,0},2ga, weighted mean deviation, 256IA(, indicator function of set A, 334Sc(, support function of set C, 337n(n.f. p). space. 399A(x), set ofdist(x, A), distance from point x to set Ae multipliers vectors334348dom f, domain of function f, 333N(μ,∑), nonmal distribution,16Nc, normal cone to set C, 337dom 9, domain of multifunction 9, 365IR, set of extended real numbers. 333o(z), cdf of standard normal distribution,epif, epigraph of function f, 333IIx, metric projection onto set X, 231epiconvergence, 377convergence in distribution, 163SN, the set of optimal solutions of the0(x,h)d order tangent set 348SAA problem. 156AVOR. Average value-at-Risk. 258Sa, the set of 8-optimal solutions of thef, set of probability measures, 306SAA problem. 181ID(A, B), deviation of set A from set Bn,N, optimal value of the Saa problem,334156IDIZ], dispersion measure of random vari-N(x), sample average function, 155able 7. 2541A(, characteristic function of set A, 334吧, expectation,361int(C), interior of set C, 336TH(A, B), Hausdorff distance between setsLa」, integer part of a∈R,219A and B. 334Isc f, lower semicontinuous hull of funcN, set of positive integers, 359tion f, 3332009/8/20pageList of notationsRc, radial cone to set C, 337C, tangent cone to set C, 337V-f(r), Hessian matrix of second orderpartial derivatives, 179a. subdifferential. 338a, Clarke generalized gradient, 336as, epsilon subdifferential, 380pos w, positive hull of matrix W, 29Pr(A), probability of event A, 360ri relative interior. 337upper semideviation, 255Le, lower semideviation, 255@R. Value-at-Risk. 25Var[X], variance of X, 149, optimal value of the true problem, 1565=(51,……,5), history of the process,{a,b},186r, conjugate of function/, 338f(x, d), generalized directional deriva-g(x, h), directional derivative, 334O,(, term, 382p-efficient point, 116lid, independently identically distributed,1562009/8/20page xlllPrefaceThe main topic of this book is optimization problems involving uncertain parametersfor which stochastic models are available. Although many ways have been proposed tomodel uncertain quantities stochastic models have proved their flexibility and usefulnessin diverse areas of science. This is mainly due to solid mathematical foundations andtheoretical richness of the theory of probabilitystochastic processes, and to soundstatistical techniques of using real dataOptimization problems involving stochastic models occur in almost all areas of scienceand engineering, from telecommunication and medicine to finance This stimulates interestin rigorous ways of formulating, analyzing, and solving such problems. Due to the presenceof random parameters in the model, the theory combines concepts of the optimization theory,the theory of probability and statistics, and functional analysis. Moreover, in recent years thetheory and methods of stochastic programming have undergone major advances. all thesefactors motivated us to present in an accessible and rigorous form contemporary models andideas of stochastic programming. We hope that the book will encourage other researchersto apply stochastic programming models and to undertake further studies of this fascinatinand rapidly developing areaWe do not try to provide a comprehensive presentation of all aspects of stochasticprogramming, but we rather concentrate on theoretical foundations and recent advances inselected areas. The book is organized into seven chapters The first chapter addresses modeling issues. The basic concepts, such as recourse actions, chance(probabilistic)constraintsand the nonanticipativity principle, are introduced in the context of specific models. Thediscussion is aimed at providing motivation for the theoretical developments in the book,rather than practical recommendationsChapters 2 and 3 present detailed development of the theory of two-stage and multistage stochastic programming problems. We analyze properties of the models and developoptimality conditions and duality theory in a rather general setting. Our analysis coversgeneral distributions of uncertain parameters and provides special results for discrete distributions, which are relevant for numerical methods. Due to specific properties of two- andmultistage stochastic programming problems, we were able to derive many of these resultswithout resorting to methods of functional analvsisThe basic assumption in the modeling and technical developments is that the proba-bility distribution of the random data is not influenced by our actions(decisions). In someapplications, this assumption could be unjustified. However, dependence of probability dis-tribution on decisions typically destroys the convex structure of the optimization problemsconsidered, and our analysis exploits convexity in a significant way

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阵列信号处理领域中常见算法的集成工具箱，MATLAB代码

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利用粒子群优化小波神经网络进行车位预测，仿真结果显示预测精度较高

非局部均值图像去噪算法的matlab实现的讨论，包括算法实现过程的转换以及matlab实现的流程图。最后则是对仿真结果的分析，分别从两个角度进行了比较：同一图像在不同噪声下的PSNR（峰值信噪比），以及在同一噪声下不同的图像下的PSNR比较，从而判断非局部均值图像去噪算法的去噪效果。

Simulink建模仿真实例详解一书详细讲解simulink建模仿真技术和方法，适合快速入门simulink建模。动态系统的计算机仿真系统与模型系统系统只指具有某些特定功能、相互联系、相互作用的元素的集合。这里的系统是指广义上的系统,泛指自然界的一切现象与过程,例如工程系统如控制系统、通讯系统等,非工程系统如股市系统、交通系统、生物系统等。系统模型系统模型是对实际系统的一种抽象,是对系统本质(或是系统的某种特性)的一种描述。模型具有与系统相似的特性。好的模型能够反映实际系统的主要特征和运动规律。模型可以分为实体模型和数学模型实体模型又称物理效应模型,是根据系统之间的相似性而建立起来的物理模型,如建筑模型等。数学模型包括原始系统数学模型和仿真系统数学模型。原始系统数学模型是对系统的原始数学描述。仿真系统数学模型是一种适合于在计算机上演算的模型,主要是指根据计算机的运算特点、仿真方式、计算方法、精度要求将原始系统数学模型转换为计算机程序。静态系统模型动态系统模型连续系统模型离散系统模型代数方程集中参数分布参数差分方程微分方程偏微分方程计算机仿真仿真的概念仿真是以相似性原理、控制论、信息技术及相关领域的有关知识为基础,以计算机和各种专用物理设备为工具,借助系统模型对真实系统进行试验的一门综合性技术。仿真分类()实物仿真:又称物理仿真。是指研制某些实体模型,使之能够重现原系统的各种状态。早期的仿真大多属于这一类优点:直观,形象,至今仍然广泛应用。缺点:投资巨大、周期长,难于改变参数,灵活性差()数学仿真:是用数学语言去描述一个系统,并编制程序在计算机上对实际系统进行研究的过程。优点:灵活性高,便于改变系统结构和参数,效率高(可以在很短时间内完成实际系统很长时间的动态演变过程),重复性好缺点:对某些复杂系统可能很难用数学模型来表达,或者难以建立其精确模型,或者由于数学模型过于复杂而难以求解()半实物仿真:又称数学物理仿真或者混合仿真。为了提高仿真的可信度或者针对一些难以建模的实体,在系统研究中往往把数学模型、物理模型和实体结合起来组成一个复杂的仿真系统,这种在仿真环节中存在实体的仿真称为半物理仿真或者半物理仿真,如飞机半实物仿真等。计算机仿真计算机仿真是在研究系统过程中根据相似性原理,利用计算机来逼真模拟研究系统。研究对象可以是实际的系统,也可以是设想中的系统。在没有计算机以前,仿真都是利用实物或者它的物理模型来迸行研究的,即物理仿真。物理仿真的优点是直接、形象、可信,缺点是模型受限、易破坏、难以重用。计算机仿真可以用于研制产品或设计系统的全过程,包括方案论证、技术指标确定、设计分析、故障处理等各个阶段。如训练飞行员、宇航员的方针工作台和仿真机舱等。仿真的三要素计算机仿真的三个基本要素是系统、模型和计算机,联系着它们的三项基本活动是模型建立、仿真模型建立(又称二次建模)和仿真试验。数学仿真采用数学模型,用数学语言对系统的特性进行描述,其工作过程是:建立系统的数学模型●建立系统仿真模型,即设计算法,并转化为计算机程序,使系统的数学模型能为计算机所接受并能在计算机上运行;运行仿真模型,进行仿真试验,再根据仿真试验的结果进步修正系统的数学模型和仿真模型与建模仿真是一种用来实现计算机仿真的软件工具。它是的一个附加组件,可用于实现各种动态系统(包括连续系统、离散系统和混合系统)的建模、分析和仿真。特点:易学易用,能够依托提供的丰富的仿真资源的应用领域()通讯与卫星系统;()航空航天系统;()生物系统;()船舶系统;()汽车系统;()金融系统;()控制系统。应用举例(原教材例子)在命令窗口中输入右图所示的模型用来口口口口模拟双质量一弹簧系统在光滑平面上受个周期力情况下的运动状态,其中周期力只作用在左边的质量块上。

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