Lectures on Stochastic Programming-Model
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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CMW500仪器编程手册
CMW500的资料不多，这是非常不错的参考资料R&s CMW 500Contents overviewContents overview1 Preparing the Instrument for Use2 Getting Started3 System Overview4 Basic Instrument functions5 Remote Control6 System Command Reference7 General Purpose RF Applications8 GSM Applications9 WCDMA Applications10 WiMAX Applications11 AnnexesNote about Faceless InstrumentsChapter 1 of this manual gives an overview of the front panel controls and connectorsof the R&s CMW 500 Wideband Radio Communication Testers with display and givesall information that is necessary to put the instrument into operation and connectexternal devices. The application examples in Chapter 2 and the following chapters arealso based on a r&S CMW 500 with displayThe measurement functionality of the two instrument types is identical. You can test allmeasurement examples reported in this manual using an r&S CMW 500 withoutdisplay that is controlled from the Graphical User Interface displayed on an externalmonitor or pcFor specific information concerning faceless instruments refer to your quick start guide.Operating Manual 1202. 3986.32-03R&s CMW 500ContentsContents1 Preparing for Use…日日画1.1 Front Panel Tour1.1.1 Utility Keys…1.1.2 Status LEDs and Standby Key1.1.3 Display…1.1.4 Softkeys and Hotkeys1.1.5 Setup Keys1.1.6 Data Entry Keys223334561.1.7 Rotary Knob and Navigation Keys1.1.8 Front panel connectors1.1.8. 1 RF Connectors1.1.8.2 LAN Connector1.183 SENSOR Connector1.184 USB Connectors1.1.8.5 AF Connectors888881.2 Rear panel tour:::::B:1.3 Putting the Instrument into Operation1.3.1 Unpacking the instrument and checking the shipment1.3.2 Instrument Setup...............001.3.3 Bench Top Operation1.3.4 Mounting in a 19 Rack121.3.5 EMI Protective measures131.3.6 Connecting the Instrument to the AC Supply131.3.7 Power on and off…131.3.8 Replacing Fuses141.3.9 Standby and ready state141.4 Maintenance15Operating manual 1202.3986.32-03R&s CMW 500Contents1.4.1 Storing and Packing151.5 Connecting External Accessories…………,…,…,…,…,…,…,………,……….151.5.1 Connecting a mouse161.5.2 Connecting a Keyboard161.5.3 Connecting a Printer1.5. 4 Connecting a monitor1.5.5 Connecting a LAN Cable788916 Starting the R&scMW500 and Shutting D。wn…,.....,.,…,,,191.7 Remote Operation in a LAN...........-.201.7.1 Assigning an IP Address201.7.2 Remote Desktop Connection221.8 Windows xP国国国面1.9 Firmware Update…,,…,,,,,,,,,",…,…222 Getting Started,…,…,…252.1 Basic tasks.…252.1.1 Accessing Dialogs252.1.2 Using Keyboard Shortcuts272.1.3 Data entr272.14 Using Front Panel Keys.……282.1.5 Using an External Keyboard2.1.6 Task bar302.2 Sample Session…312.2.1 Generating an rf signal312.2.1.1 GPRF Generator2.2.2 Measuring an RF Signal332.2.2.1 GPRE Power333 System Overview…363.1 Generators363.1.1 Generator Control363.1.2 RF Path Settings(Generators)37Operating manual 1202.3986.32-03R&s CMW 500Contents3.2 Measurements383.2.1 Measurement control383.2.2 Connection Control(Measurements393.2.3 Statistical Settings3. 2. 4 Statistical Results3.2.4.1 Statistics Type423.2.4.2 Detectors433.2.4.3Peak∨ alues.433244 Averaging…443.245 Standard deviation143.2.5 Trigger Settings453.2.6 TX Measurements453.2.6.1 Power results463.2.6.2 Modulation accuracy3.2.6.3 Adjacent Channel Power(Spectrum)493.2.6.4 Spectrum Emission Mask493.2. 6.5 Code domain power503.2.6.6 Multi-Evaluation measurements4 Basic Instrument Functions534.1.1 Startup Dialog534.2 Utility Dialogs544.2.1 Reset Dialog..4.2.2 Print Dialog554.2.3 Save/Recall Dialog564.3 Setup Dialog…,,,,…574.3.1 Activating Options584.3.2 Selftests…594.3.2.1 General test features604.3.2.2 Board Tests614.3.23 System Tests…62Operating Manual 1202.3986.32-03R&s CMW 500Contents4.3.2.4 Performing Selftests24.3.25 Selftest Parameters634.3.3 Reference Frequency654.3.3.1 Reference Frequency Settings654.3.4 Measurement Controller Dialog664.3.5 Generator Controller Dialog665 Remote Control685.1 Remote Control Operation685.1.1 Establishing and Testing a LAN Connection705.1.2 Switchover to remote control5.1.3 Return to Manual Operation715.2 Messages国国国面…725.2.1 VXI-11 Interface Messages725.2.2 GPIB Bus Interface Messages..725.2.3 Device Messages(Commands and Device Responses)735.2. 4 SCPl Command structure and syntax735.2.4.1 Common commands745.2.4.2 Instrument-Control Commands5.2.4,3 Structure of a command line765.2.4.4 Responses to Queries5.2.45 SCPI Parameters,775.2.4.6 Use of SCPl Subsystems95.3 R&s CMW Software and command structure5.3.1 General command structure5.3.2 Firmware applications815.3.3 Measurement Contexts and views5.4 Control of the instrument825.4.1 Measurement Control825.4.1.1 Measurement states and measurement control commands835.4.1.2 INITiate: : MEASurement84Operating manual 1202.3986.32-03R&s CMW 500Contents5.4.1.3 ABORt: MEASurement 8554.14sTOP:< Application> MEASurement>.,……855.4.1.5 Measurement substates855.4.2 Statistical Settings865.4.3 Retrieving Measurement Results885.4,3. FEtCh.? Command885.4.3.2 READ.? Command∴8954.33 Retrieving Single∨ alues and traces.…5.4.4 Reliability Indicator5.4.4.1 Common Reliability Indicator……5.4.5 Multi-Evaluation Measurements5.4.6 Generator control925. 4.7 RF Path Settings945.4.8 Resource and path Management∴945.4.8. 1 Basic RPM Principles5.4.8.2 Queuing of Measurements∴9654.83 Causes for task Conflicts5.4.8.4 Monitoring Measurement and Generator States995.5 Command Processing...:::::B:995.5.1 Input Unit1005.5.2 Command Recognition1005.5.3 Data base and instrument hardware1015.5.4 Status Reporting System1015.5.5 Output Un1025.6 Status Reporting System1025.6.1 Overview of status Registers1035.6.2 Structure of an SCPl Status Register…1035.6.2.1 Description of the five status register parts1045.6.3 Contents of the Status Registers1055.6.3.1 STB and sre.105Operating manual 1202.3986.32-03R&s CMW 500Contents5.6.3.2 IST Flag and pPe.1065.6. 3.3 EsR and ese∴1075.6.3 4 STATus: OPERation1085.6.3.5 STATus QUEStionable1085.6.4 Application of the status reporting s ystem1085.6.4.1 Service Request1085.6.4.2 Serial poll1095.6.4.3 Parallel poll1095.6.4.4 Query of an Instrument Status1105.64.5 Error queue.….115.6.5 Reset Values of the Status Reporting System1116 Command reference∴∴11361 Special Terms and Notation…,…………,…,…,………,…,…,,…,………………1136.2 Common commands1156.3 Instrument-Control commands.117631 MMEMory Commands…..,,,…,,……1176.3.2 Ref Frequency Commands1206.3.3 STATus Commands1216.3.4 SYSTem Commands1256.3.5 LAN Services1256.3.6 Miscellaneous Instrument Settings1286. 4 Alphabetical List of Commands System)1317 GPRF Applications1337.1 GPRF Measurements and generators1337.1.1 General Purpose RF Generato.1337.1.1.1 GPRF Generator(Constant Frequency)1337.1.1.2 Arbitrary RF Generator(Option R&S CMW-B110 A)1347.1.13 List Mode∴1357.1.2 Power measurement1357.1.2.1 Test Setup…135Operating manual 1202.3986.32-03
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