CBIR(MATLAB,HSV直方图，Haar纹理特征，GIST特征，形状特征)

纯java写的局域网斗地主游戏包括服务器端 客户端 界面 规则 5毛钱特效等采用socket网络传输凑后玩

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

1. 本文档包含：三维点云文件points.txt和C++项目文件CylinderFitting。2. 本文档通过对三维点云的圆柱拟合，得到圆柱的半径、圆柱轴线单位方向向量和轴线起始位置三个主要参数。3. 本文档算法源自“学编程的小蜜蜂”的matlab程序：http://download.csdn.net/detail/erlongz/5815557

齿轮箱故障诊断系统，源代码，和大家分享了···

哈工大丁振良老师的仪器精度理论教材，十分具有实用价值

系统的介绍了包络分析在实际中的应用，介绍了matlab进行包络分析的方法，并附有源程序供科研人员学习这是一个分式规划问题。若令则()可化为等价的线性规划问题:线性规划()的解和称为的最佳权向量,它们是使的效率值达到最大值的权向量。注意:作为线性规划的解,和不是唯一的义()若线性规划()的解满足:,则称为弱有效的;()若线性规划()的解中存在解并且则称为有效的。为了便于检验的有效性,一般考虑()的对偶模型的等式形式(带有松弛变量目具有非阿基米德无穷小)∑∑其中是项输入的松弛变量是项输出的松弛变量;是个的组合系数;;是个很小的止数(般取)。定理设线性规划(的最优解为则()若为弱有效()的;()若且则为有效()的程序由上一节知,要计算一个的相对效率值并讨论其(弱)有效性,须解一个线性规划若要计算所有的相对效率值,则须解个线性规划,其计算量比较大,一般须利用计算机进行计算。我们利用数学软件编写了解模型()和(的程序,比较方便地解决了的计算量大和计算复杂的问题是由公司用语言编写的著名的工程数学应用软件。它自牛推向市场以来,历经十几年的发展和竞争,现已成为国际认可的最优化的科技应用软件。目前,口经成为世界上诸多科技领域的基本应用软件。在国内、外的很多高等院校和科研机构已经十分普及。熟练地运用已成为晑校师生及科研人员的基本技能强大的矩阵运算能力和方便、直观的编程功能是我们选择它作为编写应用程序的原因。诚然,或是解线性规划问题的专业软件,但它们缺乏方便的编程功能和矩阵输入功能,在解一系列线性规划时,它们不如方便。此外,它们的普及程度远不如因此,我们认为是编写应用程序的最佳软件之一。所解的线性规划的标准形式是板小化问题:其中,是变量,是目标函数的系数向量,是不等式约枣的系数矩阵,是等式约束的系数矩阵,和分别是变量的下界和上界解线性规划()的语句为如果要解极大化问题,只须解极小化问题卜面,我们给出模型和(的程序。程序模型的程序)用户输入多指标输入矩阵用户输入多指标输出矩阵解线性规划,得的最佳权向量求出的相对效率值输出最佳权向量输出相对效率值输出投入权向量输出产出权向量程序模型(的程序)用户输入多指标输入矩阵用户输入多指标输出矩阵定义非阿基米德无穷小解线性规划,得的最佳权向量输出最佳权向量输出输出输出输出以上两个程序十分便于使用。用户只须输入多指标输入矩阵和输出矩阵,目可得到所需的结果。程序的应用设有某大学的同类型的五个系在一学年内的投入和产出的数据如下投教职工(人)教职工工资(万元)入运转经费(万元)毕业的本科生:(人)毕业的研究生(人)出发表的论文(篇)完成的科研项目(项)其中,运转经费指一学年內维持该系正常运转的各和费用,如行政小公费、图书资料费、差旅费等等。由程序,得到各系的相对效率值:以及各项投入和产出的权向量中定义,和至少是弱有效的和是非弱有效的。为了确认和的有效性并分析和非有效的原因,须利用模型(。由程序,得本问题的解:由以上解可看出:和的解中且松弛变量故由定理知,这几个系是相对有效的。和的非有效性也可以在以上解中看得一清二楚。以为例,根据有效性的经济意义,在不减少各项输出的前提下,构造一个新的投入的投入按比例减少到原投入的)倍,)并且(由非零的松弛变量可知)还可以进一步减少教职工工资万元、减少运转费用万元、多培养本科生人多完成项科研项目。对的非有效性可作类似的经济解释。结束语本文利用数学软件编写了便于使用的的计算程序,使计算量大和计算复杂的问题得到较好的解决。本文只对的模型进行了讨论。对于的另一个重要模型一模型,只须在模型(。中增加约東条件∑A,程序作相应的修攻即可。本文的程序为的理论研究和实际应用提供了方便、快捷的计算工具。参考文献:魏权龄评价相对有效性的方法北京:中国人民大学出版社盛旧瀚等里论、方法与应用北京:科学出版社,许波,刘征工程数学应用北京:清华大学出版社,

使用opencv实现halcon中算子find_scaled_shape_model的功能，具体功能参见博客https://blog.csdn.net/sillykog/article/details/83116793

size有js功能插件，可以通过加减按钮控制购买数量

基于java GUI的简单五子棋程序，可以局域网内联机对战，人机对战等一些地方未完善

该文件主要包含，无线传感器网络(WSN)中的拓扑模型的建立，及该网络中无线节点接收信号的衰减模型