Maximum Information Coefficient英文文献
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Preface ix Chapter 1 Introduction to Design 1 1.1 The Design Process 2 1.2 Engineering Design versus Engineering Analysis 4 1.3 Conventional versus Optimum Design Process 4 1.4 Optimum Design versus Optimal Control 6 1.5 Basic Terminology and Notation 7 1.5.1 Sets and Points 7 1.5.2 Notation for Constraints 9 1.5.3 Superscripts/Subscripts and Summation Notation 9 1.5.4 Norm/Length of a Vector 11 1.5.5 Functions 11 1.5.6 U.S.-British versus SI Units 12 Chapter 2 Optimum Design Problem Formulation 15 2.1 The Problem Formulation Process 16 2.1.1 Step 1: Project/Problem Statement 16 2.1.2 Step 2: Data and Information Collection 16 2.1.3 Step 3: Identification/Definition of Design Variables 16 2.1.4 Step 4: Identification of a Criterion to Be Optimized 17 2.1.5 Step 5: Identification of Constraints 17 2.2 Design of a Can 18 2.3 Insulated Spherical Tank Design 20 2.4 Saw Mill Operation 22 2.5 Design of a Two-Bar Bracket 24 2.6 Design of a Cabinet 30 2.6.1 Formulation 1 for Cabinet Design 30 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Inequality 62 3.2.3 Identification of Feasible Region 62 3.2.4 Plotting of Objective Function Contours 63 3.2.5 Identification of Optimum Solution 63 3.3 Use of MATLAB for Graphical Optimization 64 3.3.1 Plotting of Function Contours 64 3.3.2 Editing of Graph 64 3.4 Design Problem with Multiple Solutions 66 3.5 Problem with Unbounded Solution 66 3.6 Infeasible Problem 67 3.7 Graphical Solution for Minimum Weight Tubular Column 69 3.8 Graphical Solution for a Beam Design Problem 69 Exercises for Chapter 3 72 Chapter 4 Optimum Design Concepts 83 4.1 Definitions of Global and Local Minima 84 4.1.1 Minimum 84 4.1.2 Existence of Minimum 89 4.2 Review of Some Basic Calculus Concepts 89 4.2.1 Gradient Vector 90 4.2.2 Hessian Matrix 92 4.2.3 Taylor’s Expansion 93 4.2.4 Quadratic Forms and Definite Matrices 96 4.2.5 Concept of Necessary and Sufficient Conditions 102 xii Contents 4.3 Unconstrained Optimum Design Problems 103 4.3.1 Concepts Related to Optimality Conditions 103 4.3.2 Optimality Conditions for Functions of Single Variable 104 4.3.3 Optimality Conditions for Functions of Several Variables 109 4.3.4 Roots of Nonlinear Equations Using Excel 116 4.4 Constrained Optimum Design Problems 119 4.4.1 Role of Constraints 119 4.4.2 Necessary Conditions: Equality Constraints 121 4.4.3 Necessary Conditions: Inequality Constraints— Karush-Kuhn-Tucker (KKT) Conditions 128 4.4.4 Solution of KKT Conditions Using Excel 140 4.4.5 Solution of KKT Conditions Using MATLAB 141 4.5 Postoptimality Analysis: Physical Meaning of Lagrange Multipliers 143 4.5.1 Effect of Changing Constraint Limits 143 4.5.2 Effect of Cost Function Scaling on Lagrange Multipliers 146 4.5.3 Effect of Scaling a Constraint on Its Lagrange Multiplier 147 4.5.4 Generalization of Constraint Variation Sensitivity Result 148 4.6 Global Optimality 149 4.6.1 Convex Sets 149 4.6.2 Convex Functions 151 4.6.3 Convex Programming Problem 153 4.6.4 Transformation of a Constraint 156 4.6.5 Sufficient Conditions for Convex Programming Problems 157 4.7 Engineering Design Examples 158 4.7.1 Design of a Wall Bracket 158 4.7.2 Design of a Rectangular Beam 162 Exercises for Chapter 4 166 Chapter 5 More on Optimum Design Concepts 175 5.1 Alternate Form of KKT Necessary Conditions 175 5.2 Irregular Points 178 5.3 Second-Order Conditions for Constrained Optimization 179 5.4 Sufficiency Check for Rectangular Beam Design Problem 184 Exercises for Chapter 5 185 Chapter 6 Linear Programming Methods for Optimum Design 191 6.1 Definition of a Standard Linear Programming Problem 192 6.1.1 Linear Constraints 192 6.1.2 Unrestricted Variables 193 6.1.3 Standard LP Definition 193 Contents xiii 6.2 Basic Concepts Related to Linear Programming Problems 195 6.2.1 Basic Concepts 195 6.2.2 LP Terminology 198 6.2.3 Optimum Solution for LP Problems 201 6.3 Basic Ideas and Steps of the Simplex Method 201 6.3.1 The Simplex 202 6.3.2 Canonical Form/General Solution of Ax = b 202 6.3.3 Tableau 203 6.3.4 The Pivot Step 205 6.3.5 Basic Steps of the Simplex Method 206 6.3.6 Simplex Algorithm 211 6.4 Two-Phase Simplex Method—Artificial Variables 218 6.4.1 Artificial Variables 219 6.4.2 Artificial Cost Function 219 6.4.3 Definition of Phase I Problem 220 6.4.4 Phase I Algorithm 220 6.4.5 Phase II Algorithm 221 6.4.6 Degenerate Basic Feasible Solution 226 6.5 Postoptimality Analysis 228 6.5.1 Changes in Resource Limits 229 6.5.2 Ranging Right Side Parameters 235 6.5.3 Ranging Cost Coefficients 239 6.5.4 Changes in the Coefficient Matrix 241 6.6 Solution of LP Problems Using Excel Solver 243 Exercises for Chapter 6 246 Chapter 7 More on Linear Programming Methods for Optimum Design 259 7.1 Derivation of the Simplex Method 259 7.1.1 Selection of a Basic Variable That Should Become Nonbasic 259 7.1.2 Selection of a Nonbasic Variable That Should Become Basic 260 7.2 Alternate Simplex Method 262 7.3 Duality in Linear Programming 263 7.3.1 Standard Primal LP 263 7.3.2 Dual LP Problem 264 7.3.3 Treatment of Equality Constraints 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Genetic Algorithm for Sequencing-Type Problems 538 16.4 Applications 539 Exercises for Chapter 16 540 Chapter 17 Multiobjective Optimum Design Concepts and Methods 543 17.1 Problem Definition 543 17.2 Terminology and Basic Concepts 546 17.2.1 Criterion Space and Design Space 546 17.2.2 Solution Concepts 548 17.2.3 Preferences and Utility Functions 551 17.2.4 Vector Methods and Scalarization Methods 551 17.2.5 Generation of Pareto Optimal Set 551 17.2.6 Normalization of Objective Functions 552 17.2.7 Optimization Engine 552 17.3 Multiobjective Genetic Algorithms 552 17.4 Weighted Sum Method 555 17.5 Weighted Min-Max Method 556 17.6 Weighted Global Criterion Method 556 17.7 Lexicographic Method 558 17.8 Bounded Objective Function Method 558 17.9 Goal Programming 559 17.10 Selection of Methods 559 Exercises for Chapter 17 560 Chapter 18 Global Optimization Concepts and Methods for Optimum Design 565 18.1 Basic Concepts of Solution Methods 565 18.1.1 Basic Concepts 565 18.1.2 Overview of Methods 567 18.2 Overview of Deterministic Methods 567 18.2.1 Covering Methods 568 18.2.2 Zooming Method 568 18.2.3 Methods of Generalized Descent 569 18.2.4 Tunneling Method 571 18.3 Overview of Stochastic Methods 572 18.3.1 Pure Random Search 573 18.3.2 Multistart Method 573 18.3.3 Clustering Methods 573 18.3.4 Controlled Random Search 575 18.3.5 Acceptance-Rejection Methods 578 18.3.6 Stochastic Integration 579 18.4 Two Local-Global Stochastic Methods 579 18.4.1 A Conceptual Local-Global Algorithm 579 18.4.2 Domain Elimination Method 580 18.4.3 Stochastic Zooming Method 582 18.4.4 Operations Analysis of the Methods 583 18.5 Numerical Performance of Methods 585 18.5.1 Summary of Features of Methods 585 18.5.2 Performance of Some Methods Using Unconstrained Problems 586 Contents xix 18.5.3 Performance of Stochastic Zooming and Domain Elimination Methods 586 18.5.4 Global Optimization of Structural Design Problems 587 Exercises for Chapter 18 588 Appendix A Economic Analysis 593 A.1 Time Value of Money 593 A.1.1 Cash Flow Diagrams 594 A.1.2 Basic Economic Formulas 594 A.2 Economic Bases for Comparison 598 A.2.1 Annual Base Comparisons 599 A.2.2 Present Worth Comparisons 601 Exercises for Appendix A 604 Appendix B Vector and Matrix Algebra 611 B.1 Definition of Matrices 611 B.2 Type of Matrices and Their Operations 613 B.2.1 Null Matrix 613 B.2.2 Vector 613 B.2.3 Addition of Matrices 613 B.2.4 Multiplication of Matrices 613 B.2.5 Transpose of a Matrix 615 B.2.6 Elementary Row–Column Operations 616 B.2.7 Equivalence of Matrices 616 B.2.8 Scalar Product–Dot Product of Vectors 616 B.2.9 Square Matrices 616 B.2.10 Partitioning of Matrices 617 B.3 Solution of n Linear Equations in n Unknowns 618 B.3.1 Linear Systems 618 B.3.2 Determinants 619 B.3.3 Gaussian Elimination Procedure 621 B.3.4 Inverse of a Matrix: Gauss-Jordan Elimination 625 B.4 Solution of m Linear Equations in n Unknowns 628 B.4.1 Rank of a Matrix 628 B.4.2 General Solution of m ¥ n Linear Equations 629 B.5 Concepts Related to a Set of Vectors 635 B.5.1 Linear Independence of a Set of Vectors 635 B.5.2 Vector Spaces 639 B.6 Eigenvalues and Eigenvectors 642 B.7 Norm and Condition Number of a Matrix 643 B.7.1 Norm of Vectors and Matrices 643 B.7.2 Condition Number of a Matrix 644 Exercises for Appendix B 645 Appendix C A Numerical Method for Solution of Nonlinear Equations 647 C.1 Single Nonlinear Equation 647 C.2 Multiple Nonlinear Equations 650 Exercises for Appendix C 655 xx Contents Appendix D Sample Computer Programs 657 D.1 Equal Interval Search 657 D.2 Golden Section Search 660 D.3 Steepest Descent Method 660 D.4 Modified Newton’s Method 669 References 675 Bibliography 683 Answers to Selected Problems 687 Index 695 Contents
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已经博主授权,源码转载自 https://pan.quark.cn/s/a4b39357ea24 题目描述 任意给定 n 个整数，求这 n 个整数序列的和、最小值、最大值 输入描述 输入一个整数n，代表接下来输入整数个数，n<=100,接着输入n个整数，整数用int表示即可。 输出描述 输出整数序列的和、最小值、最大值。 用空格隔开，占一行 样例输入 2 1 2 样例输出 3 1 2 提交代码 自己编写的基础知识代码绝对真实可靠已认证核对过
Delphi 13.1控件之idman643build2.exe
Delphi 13.1控件之idman643build2.exe
mpuziliao xuexi
mpuziliao xuexi
基于ARIMA电价预测，并计算置信区间研究（Matlab代码实现）
内容概要：本文系统研究了基于ARIMA模型的电价预测方法，并结合Matlab代码实现了对未来电价的短期预测及预测结果的不确定性量化分析，重点在于构建置信区间以提升预测的可靠性。文章详细阐述了ARIMA模型在电力市场价格序列建模中的应用流程，涵盖数据预处理、平稳性检验（如ADF检验）、模型识别（ACF/PACF分析）、参数估计、模型诊断（残差白噪声检验）以及预测可视化等关键步骤。通过引入预测误差的统计分布特性，进一步计算出不同置信水平下的置信区间，为电力市场参与者提供更具决策参考价值的价格趋势判断。该方法适用于具有明显时间依赖性和波动特征的电价数据，具有较强的实用性和可操作性。; 适合人群：具备一定统计学基础和Matlab编程能力，从事电力系统运行、能源经济分析、电力市场交易及相关领域的科研人员与工程技术从业者，尤其适合高等院校电力、自动化、经济管理等专业的研究生及高年级本科生开展课题研究或课程设计。; 使用场景及目标：①应用于电力市场的短期电价预测，辅助发电商、售电公司制定竞价策略；②支持微电网、虚拟电厂等新型主体参与电力市场时的风险评估与优化调度；③作为高校教学案例，帮助学生掌握时间序列建模的基本理论与实证分析技能；④为含高比例新能源接入的电力系统提供价格波动风险的量化工具，支撑市场机制设计与政策制定。; 阅读建议：建议读者结合所提供的Matlab代码逐行运行并调试，重点关注数据差分处理、模型阶数确定（AIC/BIC准则）及残差诊断环节，建议尝试替换不同的实际电价数据集进行模型迁移验证，深入理解ARIMA建模过程中各环节的作用与敏感性，同时加强对置信区间构建原理的数学推导与解释能力。
课程总结2026.pdf
课程总结2026.pdf
IMG_20260629_163252.jpg
IMG_20260629_163252.jpg
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