美国数学建模单词

1、1 马氏链2 遗传算法,represent 代表present展现demonstrate表明handle处理For each case对于每一种情况resistance阻力therefore因此a safety margin安全裕cost-effective成本效益好的column：纵列inherently天性的be content to满足于algorithm运算法则contradict反驳streamline;使合理化，使成流线型metrics;度量terminology术语capital，uppercase大写字母upper-left左上角that is significant but

2、not too large，不是很大的量。involves many complex interactions 包括许多复杂的内部作用。As a convention,按照惯例we aim to create an algorithm with the following properties:我们可以得出具备以下特性的一种算法。These indices are related by the following functions:这些量之间有下面一些关系式！决定：determine，decide，design 进一步：Furthermore in addition 另外取决于：depend

3、 on 从那时以来since then估计estimatederive from来源于使之服从于 subject to说明 explain Criteria 标准Similarly 同样的Initial modelling最初模型rough 粗糙的thus 这样adjustment factor调整因素remove 去除 eliminate calibrate 调整Recommendation 推荐信problematic 有问题的dynamic ratio 动态参数flow rates 流动比例a fixed constant 固定的常数flowchart 流程图depends only o

4、n 仅仅取决于weighting 加权real root 实根that is 即apart from 除了（相当于besides）empirical 凭经验的refine 改进Prior to the start of，在开始之前， before coherently 连贯性transition 过渡 转变extrapolated 推测informed decision 明智的决定out of 由于 ，用 材料rigid body 刚体运动intuitively 凭直觉的respectively 相应的flaw 瑕疵postulate 假设impending 即将发生的recede 后退ass

5、ist in 帮助note that 既然基本框架summary 11 Introduction 31.1 Statement of Problem . . . . . . . . . 31.2 Relevance of Sudoku . . . . . . . . . 31.3 Goals . . . . . . . . . . . . . . . . . . 31.4 Rules of Sudoku . . . . . . . . . . . . 31.5 Terminology and Notation . . . . . . 31.6 Indexing . . . . . . . .

6、. . . . . . . . 41.7 Formal Rules of Sudoku . . . . . . . 51.8 Example Puzzles . . . . . . . . . . . 52 Background 52.1 Common Solving Techniques . . . . 52.2 Previous Works . . . . . . . . . . . . 73 Metric Design 103.1 Overview . . . . . . . . . . . . . . . . 103.2 Assumptions . . . . . . . . . .

7、. . . . 103.3 Mathematical Basis for WNEF . . . 104 Metric Calibration and Testing 114.1 Control Puzzle Sources . . . . . . . . 114.2 Testing Method . . . . . . . . . . . . 12Puzzles . . . . . . . . . . . 124.3 Choice of Weight Function. . . . . . . 125 Generator Algorithm 125.1 Overview . . . . . .

8、 . . . . . . . . . . 125.2 Detailed Description . . . . . . . . . 145.3 Pseudocode . . . . . . . . . . . . . . . 155.4 Complexity Analysis . . . . . . . . . 18Puzzle Generation . . . . . . . 18Testing and Random Filling . 185.5 Testing . . . . . . . . . . . . . . . . . 19Choices . . . . . . . . . .

9、196 Strengths and Weaknesses 197 Conclusions 21References 211 Source Code 232 Screenshots of Puzzle Generator 62List of Figures1 Demonstration of indexing schemes. 62 Puzzle generated by WebSudoku(ranked as “Easy”. . . . . . . . . . . 63 Top 1465 Number 77. . . . . . . . . . 74 An example of a hand-

10、made Nikolipuzzle. . . . . . . . . . . . . . . . . . 75 Example of the Naked Pair rule. . . 86 Example of the Naked Triplet rule. . 87 Example of the Hidden Pair rule. . . 88 Example of the Hidden Triplet rule. 99 Example of the Multi-Line rule. . . . 910 Examples of choice histograms. . . . 1111 WNEF for control puzzles by difficulty. . . . . . . . . . . . . . . . . . 1312 WNEF correlations for variousweighting functions. . . . . . . . . . . 1313 Running time as a function of th