Cluster algebras II Finite type classification
人工智能领域中英文专有名词汇总

名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱(共包含二级节点15 个,三级节点93 个)间序列分析)监督学习)领域 二级分类 三级分类。
《机器学习基石》课程笔记12 -- Nonlinear Transformation

林轩田《机器学习基石》课程笔记12NonlinearTransformation上一节课,我们介绍了分类问题的三种线性模型,可以用来解决binary classification和multiclass classification问题。
本节课主要介绍非线性的模型来解决分类问题。
一、Quadratic Hypothesis之前介绍的线性模型,在2D平面上是一条直线,在3D空间中是一个平面。
数学上,我们用线性得分函数s来表示:。
其中,x为特征值向量,w为权重,s是线性的。
线性模型的优点就是,它的VC Dimension比较小,保证了。
但是缺点也很明显,对某些非线性问题,可能会造成很大,虽然,但是也造成很大,分类效果不佳。
为了解决线性模型的缺点,我们可以使用非线性模型来进行分类。
例如数据集D不是线性可分的,而是圆形可分的,圆形内部是正类,外面是负类。
假设它的hypotheses可以写成:基于这种非线性思想,我们之前讨论的PLA、Regression问题都可以有非线性的形式进行求解。
下面介绍如何设计这些非线性模型的演算法。
还是上面介绍的平面圆形分类例子,它的h(x)的权重w0=0.6,w1=1,w2=1,但是h(x)的特征不是线性模型的,而是。
我们令,,,那么,h(x)变成:这种的转换可以看成是x空间的点映射到z空间中去,而在z域中,可以用一条直线进行分类,也就是从x空间的圆形可分映射到z空间的线性可分。
z域中的直线对应于x域中的圆形。
因此,我们把这个过程称之为特征转换(Feature Transform)。
通过这种特征转换,可以将非线性模型转换为另一个域中的线性模型。
已知x域中圆形可分在z域中是线性可分的,那么反过来,如果在z域中线性可分,是否在x域中一定是圆形可分的呢?答案是否定的。
由于权重向量w取值不同,x域中的hypothesis可能是圆形、椭圆、双曲线等等多种情况。
consensusclusterplus clusteralg参数

consensusclusterplus clusteralg参数(原创版)目录1.概述2.ConsensusClusterPlus 算法3.ClusterAlg 参数4.参数详解5.实际应用6.总结正文1.概述在数据分析和机器学习领域,聚类算法是一种重要的无监督学习方法,它可以将数据集中的相似数据点归为一类。
其中,ConsensusClusterPlus 算法和 ClusterAlg 参数是聚类分析中非常关键的概念。
本文将详细介绍这两个概念以及它们的实际应用。
2.ConsensusClusterPlus 算法ConsensusClusterPlus(CCP)算法是一种基于图的聚类方法,它通过构建数据点之间的相似性图来寻找最优聚类。
CCP 算法的核心思想是基于图论中的最短路径问题,将聚类问题转化为求解最短路径问题。
该算法具有良好的可扩展性和较高的聚类准确性,适用于大规模数据集和高维数据。
3.ClusterAlg 参数在 ConsensusClusterPlus 算法中,ClusterAlg 是一个关键参数,用于指定聚类算法的类型。
ConsensusClusterPlus 支持多种聚类算法,如 K-means、DBSCAN、OPTICS 等。
通过修改 ClusterAlg 参数,可以实现对不同聚类算法的切换,以满足不同场景下的聚类需求。
4.参数详解ConsensusClusterPlus 算法的 ClusterAlg 参数有以下几种常见类型:(1)K-means:K-means 是最常用的聚类算法之一,它通过计算数据点之间的距离来将数据点分为 K 个簇。
K-means 聚类算法的参数包括 K (聚类数量)和 max_iter(最大迭代次数)。
(2)DBSCAN:DBSCAN 是一种基于密度的聚类算法,它通过计算数据点的密度来将数据点分为不同的簇。
DBSCAN 聚类算法的参数包括 eps(邻域大小)、min_samples(最小样本数)和 max_iter(最大迭代次数)。
代数中常用英语词汇

(0,2) 插值||(0,2) interpolation0#||zero-sharp; 读作零井或零开。
0+||zero-dagger; 读作零正。
1-因子||1-factor3-流形||3-manifold; 又称“三维流形”。
AIC准则||AIC criterion, Akaike information criterionAp 权||Ap-weightA稳定性||A-stability, absolute stabilityA最优设计||A-optimal designBCH 码||BCH code, Bose-Chaudhuri-Hocquenghem codeBIC准则||BIC criterion, Bayesian modification of the AICBMOA函数||analytic function of bounded mean oscillation; 全称“有界平均振动解析函数”。
BMO鞅||BMO martingaleBSD猜想||Birch and Swinnerton-Dyer conjecture; 全称“伯奇与斯温纳顿-戴尔猜想”。
B样条||B-splineC*代数||C*-algebra; 读作“C星代数”。
C0 类函数||function of class C0; 又称“连续函数类”。
CA T准则||CAT criterion, criterion for autoregressiveCM域||CM fieldCN 群||CN-groupCW 复形的同调||homology of CW complexCW复形||CW complexCW复形的同伦群||homotopy group of CW complexesCW剖分||CW decompositionCn 类函数||function of class Cn; 又称“n次连续可微函数类”。
Cp统计量||Cp-statisticC。
群的概念教学中几个有限生成群的例子

群的概念教学中几个有限生成群的例子霍丽君(重庆理工大学理学院重庆400054)摘要:群的概念是抽象代数中的最基本的概念之一,在抽象代数课程的教学环节中融入一些有趣的群例,借助于这些较为具体的群例来解释抽象的群理论,对于激发学生的学习兴趣以及锻炼学生的数学思维能力等方面都会起到一定的积极作用。
该文介绍了一种利用英文字母表在一定的规则下构造的有限生成自由群的例子,即该自由群的同音商,称为英语同音群。
此外,该文结合线性代数中的矩阵相关知识,给出了有限生成群SL2(Z )以及若于有限生成特殊射影线性群的例子。
关键词:有限生成群英语同音群一般线性群特殊射影线性群中图分类号:O151.2文献标识码:A文章编号:1672-3791(2022)03(b)-0165-04Several Examples of Finitely Generated Groups in the ConceptTeaching of GroupsHUO Lijun(School of Science,Chongqing University of Technology,Chongqing,400054China)Abstract:The concept of group is one of the most basic concepts in abstract algebra.Integrating some interesting group examples into the teaching of abstract algebra course and explaining the abstract group theory with the help of these more specific group examples will play a positive role in stimulating students'learning interest and training students'mathematical thinking ability.In this paper,we introduce an example of finitely generated free group by using the English alphabet under some certain rules,which is called homophonic quotients of free groups,or briefly called English homophonic group.In addition,combined with the theory of matrix in linear algebra,we give some examples of about finitely generated group SL_2(Z)and finitely generated special projective linear groups.Key Words:Group;Finitely generated group,English homophonic group;General linear group;Special projective linear group1引言及准备知识群是代数学中一个最基本的代数结构,群的概念已有悠久的历史,最早起源于19世纪初叶人们对代数方程的研究,它是阿贝尔、伽罗瓦等著名数学家对高次代数方程有无公式解问题进行探索的结果,有关群的理论被公认为是19世纪最杰出的数学成就之一[1-2]。
顶点算子代数

顶点算子代数
顶点算子代数(Vertex Operator Algebra,简称VOA)是一种数
学结构,它起源于物理学中的弦理论,但现在已成为数学领域中一个
独立的研究方向。
VOA是一种代数结构,它在点运算及对称性方面表现出特殊的性质。
与一般的代数结构不同,VOA同时包含了算子和顶点两种元素,并且还具有自同构、自反等性质。
它在弦理论和两维共形场论中扮演着重要
的角色,但它的研究已经远远超出了物理学领域。
VOA的发展主要源于数学家们对弦理论中的数学结构的研究。
早期的VOA主要研究对象是双重态代数(Vertex Algebra),以及它们的
扩张(Extended Vertex Algebra)。
在这些代数中,算子代表了弦的
振动模式,而顶点则代表了弦的幺正构形。
现今,VOA已经成为一门独立的数学课题。
数学家们对VOA的研究涉及到数论、代数几何、微分几何等多个领域。
VOA也被用于解决其他数学问题,如Kepler猜想和数学物理学中的量子场论等。
VOA研究的主要方向包括结构理论、表示理论和模理论。
在结构理论方面,数学家们寻求了解VOA的基本性质、对称性及其自共轭性、
结合律、交换律、分配律等。
在表示理论方面,数学家们研究了VOA
的表示及其单位表示。
在模理论方面,数学家们研究了VOA的模及其
分类。
VOA的研究成果已经得到广泛的应用,不仅在物理学领域,也在计算机科学、加密学、组合数学等领域得到应用。
VOA的研究不仅促进了数学领域的发展,还拓宽了人类对自然界和世界的认识。
algebra2 知识点总结

algebra2 知识点总结Linear Equations and FunctionsOne of the fundamental concepts in Algebra 2 is linear equations and functions. A linear equation is an equation of the form y = mx + b, where m is the slope and b is the y-intercept. Students learn how to graph linear equations, find the slope and y-intercept, and solve systems of linear equations using various methods such as substitution, elimination, and graphing.Functions are a core concept in Algebra 2, and students study various types of functions such as linear, quadratic, exponential, and logarithmic functions. They learn how to analyze the behavior of functions, find their domain and range, and determine whether a function is even, odd, or periodic. Students also explore transformations of functions, such as shifts, stretches, and reflections, and how they affect the graph of a function.Inequalities and Absolute Value EquationsIn Algebra 2, students also study inequalities and absolute value equations. They learn how to solve and graph linear inequalities, quadratic inequalities, rational inequalities, and absolute value inequalities. They also explore the concept of compound inequalities and how to solve systems of inequalities.Absolute value equations are another important topic in Algebra 2. Students learn how to solve and graph absolute value equations, as well as inequalities involving absolute value expressions. They also study the properties of absolute value functions and their applications in real-life scenarios.Polynomials and Polynomial FunctionsPolynomials are algebraic expressions that consist of variables and coefficients, combined using addition, subtraction, and multiplication. In Algebra 2, students learn how to add, subtract, multiply, and divide polynomials, as well as factor and solve polynomial equations. They also study the properties of polynomial functions, such as end behavior, zeros, and turning points.Students delve into advanced topics such as polynomial long division, synthetic division, the remainder theorem, and the factor theorem. They also explore the relationship between polynomial functions and their graphs, and how to use this information to solve real-world problems.Rational Expressions and Rational FunctionsRational expressions are fractions that contain polynomials in the numerator and denominator. In Algebra 2, students learn how to simplify, multiply, divide, add, and subtract rational expressions, as well as solve rational equations. They also study the properties of rational functions, such as asymptotes, intercepts, and end behavior.Students explore the relationship between rational functions and their graphs, and how to use this information to analyze and solve real-world problems. They also study advanced topics such as partial fraction decomposition, complex fractions, and applications of rational functions in areas such as economics, physics, and engineering.Exponential and Logarithmic FunctionsExponential and logarithmic functions are essential in Algebra 2, and students learn how to solve exponential and logarithmic equations, as well as graph exponential and logarithmic functions. They study the properties of exponential and logarithmic functions, such as growth and decay, domain and range, and asymptotic behavior.Students also explore the relationship between exponential and logarithmic functions, and how to use this information to solve real-world problems. They study applications of exponential and logarithmic functions in areas such as finance, population growth, radioactive decay, and pH levels.Sequences and SeriesSequences and series are important topics in Algebra 2, and students learn how to find the nth term of a sequence, as well as the sum of a finite or infinite series. They study arithmetic sequences and series, geometric sequences and series, and other types of sequences such as Fibonacci and recursive sequences.Students explore the properties of sequences and series, such as common difference, common ratio, and convergence. They also study applications of sequences and series in areas such as finance, physics, and computer science.Complex NumbersComplex numbers are numbers of the form a + bi, where a and b are real numbers, and i is the imaginary unit (√-1). In Algebra 2, students learn how to perform operations with complex numbers, such as addition, subtraction, multiplication, division, and simplification. They also study the properties of complex numbers, such as the conjugate, modulus, and argument.Students explore the relationship between complex numbers and their graphs on the complex plane, and how to use this information to solve equations involving complex numbers. They also study applications of complex numbers in areas such as electrical engineering, signal processing, and quantum mechanics.Matrices and DeterminantsMatrices are arrays of numbers arranged in rows and columns, and they are used to represent and solve systems of linear equations. In Algebra 2, students learn how to add, subtract, multiply, and invert matrices, as well as find the determinant of a matrix. They also study the properties of matrices, such as the identity matrix, transpose, and rank.Students explore the relationship between matrices, determinants, and systems of linear equations, and how to use this information to solve real-world problems. They also study applications of matrices in areas such as computer graphics, cryptography, and economics.Conic SectionsConic sections are curves obtained by intersecting a cone with a plane, and they include the circle, ellipse, parabola, and hyperbola. In Algebra 2, students learn how to graph and analyze conic sections, as well as find their equations given certain properties.Students study the properties of conic sections, such as the focus, directrix, eccentricity, and asymptotes. They also explore the relationship between conic sections and their equations, and how to use this information to solve real-world problems. They study applications of conic sections in areas such as astronomy, engineering, and architecture.ConclusionAlgebra 2 is a challenging but rewarding branch of mathematics that builds upon the concepts learned in Algebra 1. In this article, we have provided a comprehensive summary of the key topics in Algebra 2, including linear equations and functions, inequalities and absolute value equations, polynomials and polynomial functions, rational expressions and rational functions, exponential and logarithmic functions, sequences and series, complex numbers, matrices and determinants, and conic sections.By mastering these topics, students will develop a deeper understanding of algebraic concepts and techniques, as well as their applications in various fields such as science, engineering, economics, and finance. Algebra 2 is an essential foundation for further study in mathematics and related disciplines, and it provides students with the analytical and problem-solving skills necessary for success in the modern world.。
二分法的英文解释

二分法的英文解释The binary search algorithm is a fundamental concept in computer science and mathematics. It is a powerful and efficient technique used to locate a specific item within a sorted collection of data. In this article, we will delve into the details of the binary search algorithm, exploring its mechanics, applications, and complexities.At its core, the binary search algorithm operates by repeatedly dividing the search interval in half. It begins by comparing the target value with the middle element of the array. If the target value matches the middle element, the search is complete, and the index of the element is returned. If the target value is less than the middle element, the search continues in the lower half of the array. Conversely, if the target value is greater than the middle element, the search proceeds in the upper half of the array. This process is repeated until the target value is found or until the search interval is empty.One of the key requirements for the binary search algorithm to work is that the data collection must be sorted in ascending order. This prerequisite enables the algorithm to exploit the properties of the sorted array, significantly reducing the search space with each iteration. By systematically eliminating half of the remaining elements at each step, binary search achieves a logarithmic time complexity of O(log n), where n is the number of elements in the array. This efficiency makes binary search particularly suitable for large datasets where linear search algorithms would be impractical.The binary search algorithm finds its applications in various domains, including but not limited to:1. Searching: Binary search is commonly used to quickly locate elements in sorted arrays or lists. Its efficiency makes it indispensable for tasks such as searching phone directories, dictionaries, or databases.2. Sorting: While binary search itself is not a sorting algorithm, it can be combined with sorting algorithms like merge sort or quicksort to efficiently search for elements in sorted arrays.3. Computer Science: Binary search serves as a foundational concept in computer science education, providing students with a fundamental understanding of algorithm design and analysis.4. Game Development: Binary search is utilized in game development for tasks such as collision detection, pathfinding, and AI decision-making.Despite its efficiency and versatility, binary search does have certain limitations. One significant constraint is that the data collection must be sorted beforehand, which can incur additional preprocessing overhead. Additionally, binary search is not well-suited for dynamic datasets that frequently change, as maintaining the sorted order becomes non-trivial.In conclusion, the binary search algorithm is a powerful tool for efficiently locating elements within sorted arrays. Its logarithmic time complexity and widespread applications make it a fundamental concept in computer science and mathematics. By dividing the search space in half with each iteration, binary search demonstrates the elegance and efficiency of algorithmic design. Whether used in searching, sorting, or other computational tasks, the binary search algorithm remains a cornerstone of algorithmic problem-solving.。
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2
SERGEY FOMIN AND ZELEVINSKY
1. Introduction and main results 1.1. Introduction. The origins of cluster algebras, first introduced in [9], lie in the desire to understand, in concrete algebraic and combinatorial terms, the structure of “dual canonical bases” in (homogeneous) coordinate rings of various algebraic varieties related to semisimple groups. Several classes of such varieties—among them Grassmann and Schubert varieties, base affine spaces, and double Bruhat cells—are expected (and in many cases proved) to carry a cluster algebra structure. This structure includes the description of the ring in question as a commutative ring generated inside its ambient field by a distinguished family of generators called cluster variables. Even though most of the rings of interest to us are finitely generated, their set of cluster variables may well be infinite. A cluster algebra has finite type if it has a finite number of cluster variables. The main result of this paper (Theorem 1.4) provides a complete classification of the cluster algebras of finite type. This classification turns out to be identical to the Cartan-Killing classification of semisimple Lie algebras and finite root systems. This result is particularly intriguing since in most cases, the symmetry exhibited by the Cartan-Killing type of a cluster algebra is not apparent at all from its geometric realization. For instance, the coordinate ring of the base affine space of the group SL5 turns out to be a cluster algebra of the Cartan-Killing type D6 . Other examples of similar nature can be found in Section 12, in which we show how cluster algebras of types ABCD arise as coordinate rings of some classical algebraic varieties. In order to understand a cluster algebra of finite type, one needs to study the combinatorial structure behind it, which is captured by its cluster complex. Roughly speaking, it is defined as follows. The cluster variables for a given cluster algebra are not given from the outset but are obtained from some initial “seed” by an explicit process of “mutations”; each mutation exchanges a cluster variable in the current seed by a new cluster variable according to a particular set of rules. In a cluster algebra of finite type, this process “folds” to produce a finite set of seeds, each containing the same number n of cluster variables (along with some extra information needed to perform mutations). These n-element collections of cluster variables, called clusters, are the maximal faces of the (simplicial) cluster complex. In Theorem 1.13, we identify this complex as the dual simplicial complex of a generalized associahedron associated with the corresponding root system. These complexes (indeed, convex polytopes [7]) were introduced in [11] in relation to our proof of Zamolodchikov’s periodicity conjecture for algebraic Y -systems. A generalized associahedron of type A is the usual associahedron, or the Stasheff polytope [25]; in types B and C , it is the cyclohedron, or the Bott-Taubes polytope [5, 24]. One of the crucial steps in our proof of the classification theorem is a new combinatorial characterization of Dynkin diagrams. In Section 8, we introduce an equivalence relation, called mutation equivalence, on finite directed graphs with weighted edges. We then prove that a connected graph Γ is mutation equivalent to an orientation of a Dynkin diagram if and only if every graph that is mutation equivalent to Γ has all edge weights ≤ 3. We do not see a direct way to relate this description to any previously known characterization of the Dynkin diagrams. We already mentioned that the initial motivation for the study of cluster algebras came from representation theory; see [27] for a more detailed discussion of the representation-theoretic context. Another source of inspiration was Lusztig’s
arXiv:math/0208229v2 [math.RA] 12 Mar 2003
CLUSTER ALGEBRAS II: FINITE TYPE CLASSIFICATION
SERGEY FOMIN AND ANDREI ZELEVINSKY
Contents 1. Introduction and main results 1.1. Introduction 1.2. Basic definitions 1.3. Finite type classification 1.4. Cluster variables in the finite type 1.5. Cluster complexes 1.6. Organization of the paper 2. Cluster algebras via pseudomanifolds 2.1. Pseudomanifolds and geodesic loops 2.2. Sufficient conditions for finite type 3. Generalized associahedra 4. Proof of Theorem 1.5 5. Proofs of Theorems 1.9 and 1.11–1.13 6. Proof of Theorem 1.10 7. 2-finite matrices 8. Diagram mutations 9. Proof of Theorem 8.6 9.1. Shape-preserving diagram mutations 9.2. Taking care of the trees 9.3. Taking care of the cycles 9.4. Completing the proof of Theorem 8.6 10. Proof of Theorem 1.7 11. On cluster algebras of geometric type 11.1. Geometric realization criterion 11.2. Sharpening the Laurent phenomenon 12. Examples of geometric realizations of cluster algebras 12.1. Type A1 12.2. Type An (n ≥ 2) 12.3. Types Bn and Cn 12.4. Type Dn Acknowledgments References 2 2 3 5 6 7 8 8 8 10 11 14 19 21 25 28 30 31 31 34 35 36 37 37 38 39 39 40 43 47 49 49