奧林匹克數學中的組合問題

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【書籍印製時偶有輕微墨點,不介意再下單】 奧林匹克數學中的組合問題 ISBN13：9789571205991 出版社：曉園 作者：張堯 裝訂：平裝 出版日：2005/05/01 內容簡介 數學競賽中出現的組合問題往往表達形式上簡單明了，而求解這些問題卻需要敏銳的洞察力、豐富的想像力和必要的技巧，通常沒有一個固定的解題模式可遵循，而且各種難易程度不同的問題都非常富有，所以在各類不同程度的智力訓練和數學競賽中，大都不離組合問題。 本書重點討論和研究在數學競賽中經常出現的組合問題，除了必要的組合數學的有關知識外，著重介紹了解這類問題的一些基本方法，在介紹解題方法時，配備了一些相當於全國高中數學聯賽水平的例題。每章最後一節為典型例題解題分析，所配備的例題相當於CMO和IMO的水平。 目錄 第一章 組合數學中的計數問題 第二章 組合恆等式和組合問題中的不等式 第三章 存在性問題 第四章 組合最值問題 第五章 操作變換問題 第六章 組合幾何中的問題 第七章 圖論中的問題 參考解答

線性代數習題詳解 (4版)

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【書籍印製時偶有輕微墨點,不介意再下單】 【中文翻譯書】 書名 : 線性代數習題詳解 第四版 原文書名 : Linear Algebras 4/E 原著 : Stephen H. Friedberg 作者 : 劉勇 ISBN： 9789571206356 目錄 第一章 向題空間 第二章 線性變換與矩陣 第三章 基本矩陣運算與線性方程組 第四章 行列式 第五章 對角化 第六章 內積空間 第七章 正準形式

線性代數導論 (8版)

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書名：線性代數導論(8版) 作者：Kolman(呂金河) 出版社：華泰 出版日期：2005/08/00 ISBN：9789576095962 內容簡介 　　本書介紹線性代數的主要課題及其重要的應用，內容包含線性方程組與矩陣、行列式、n維向量、向量空間、特徵值與特徵向量、線性轉換的特性與應用等，文中集結了所有基本線性代數的精華主題，同時為使數學推導的抽象程度降到最低，有時會省略較困難的證明，避免使用微積分，而用例題來說明相關性質，強調各個線性代數主題的計算及幾何觀念。書中每章最後均包含一個摘要性的重點整理做為重要觀念的複習，並輔以一組補充習題及章節測驗做為了解整章程度的自我考驗，書末還附單數題習題解答及章節測驗的所有答案。綜合上述，本譯著適合需要學習線性代數或相關理工科系的同學閱讀，並推薦給在大一、大二教授線性代數的教師使用。 目錄 第1章　線性方程式和矩陣 第2章　行列式 第3章　Rn上的向量 第4章　R2及R3上向量的應用 第5章　實數向量空間 第6章　特徵值、特徵向量及對角線化 第7章　線性轉換和矩陣

Linear Algebra for Data Science

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This book serves as an introduction to linear algebra for undergraduate students in data science, statistics, computer science, economics, and engineering. The book presents all the essentials in rigorous (proof-based) manner, describes the intuition behind the results, while discussing some applications to data science along the way. The book comes with two parts, one on vectors, the other on matrices. The former consists of four chapters: vector algebra, linear independence and linear subspaces, orthonormal bases and the Gram–Schmidt process, linear functions. The latter comes with eight chapters: matrices and matrix operations, invertible matrices and matrix inversion, projections and regression, determinants, eigensystems and diagonalizability, symmetric matrices, singular value decomposition, and stochastic matrices. The book ends with the solution of exercises which appear throughout its twelve chapters. Request Inspection Copy Sample Chapter(s) Preface Chapter 1: Vector Algebra Chapter 2: Linear Independence and Linear Subspaces Contents: Preface Vectors: Vector Algebra Linear Independence and Linear Subspaces Orthonormal Bases and the Gram–Schmidt Process Linear Functions Matrices: Matrices and Matrix Operations Invertible Matrices and the Inverse Matrix The Pseudo-Inverse Matrix, Projections and Regression Determinants Eigensystems and Diagonalizability Symmetric Matrices Singular Value Decomposition Stochastic Matrices Solutions to Exercises Bibliography Index Readership: Undergraduate course in linear algebra as part of a major in data science, statistics, computer science, economics, and engineering.

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