Introduction to Linear Algebra, 6/e (Paperback)

Description
Linear algebra now rivals or surpasses calculus in importance for people working in quantitative fields of all kinds: engineers, scientists, economists and business people. Gilbert Strang has taught linear algebra at MIT for more than 50 years and the course he developed has become a model for teaching around the world. His video lectures on MIT OpenCourseWare have been viewed over ten million times and his twelve textbooks are popular with readers worldwide.

This sixth edition of Professor Strang's most popular book, Introduction to Linear Algebra, introduces the ideas of independent columns and the rank and column space of a matrix early on for a more active start. Then the book moves directly to the classical topics of linear equations, fundamental subspaces, least squares, eigenvalues and singular values – in each case expressing the key idea as a matrix factorization. The final chapters of this edition treat optimization and learning from data: the most active application of linear algebra today. Everything is explained thoroughly in Professor Strang's characteristic clear style. It is sure to delight and inspire the delight and inspire the next generation of learners.

描述

線性代數現在在各種量化領域的從業人員中，如工程師、科學家、經濟學家和商人，的重要性已經與微積分相媲美甚至超越。Gilbert Strang在麻省理工學院教授線性代數已經超過50年，他所開發的課程已成為全球教學的典範。他在MIT OpenCourseWare上的視頻講座已經被觀看超過一千萬次，他的十二本教科書在全球讀者中非常受歡迎。



這本教授Strang最受歡迎的書籍的第六版，《線性代數導論》，早期引入了矩陣的獨立列和秩以及列空間的概念，以更積極的方式開始。然後，書籍直接轉向線性方程、基本子空間、最小二乘法、特徵值和奇異值等經典主題，並將每個關鍵思想表達為矩陣分解。本版的最後幾章討論了優化和從數據中學習：這是當今線性代數最活躍的應用領域。所有內容都以Strang教授獨特的清晰風格進行了詳細解釋。它肯定會讓下一代學習者感到愉悅和啟發。

Table of Contents
1 Vectors and Matrices
2 Solving Linear Equations Ax = b
3 The Four Fundamental Subspaces
4 Orthogonality
5 Determinants
6 Eigenvalues and Eigenvectors
7 The Singular Value Decomposition (SVD)
8 Linear Transformations
9 Linear Algebra in Optimization
10 Learning from Data

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目錄

1 向量和矩陣

2 解線性方程 Ax = b

3 四個基本子空間

4 正交性

5 行列式

6 特徵值和特徵向量

7 奇異值分解 (SVD)

8 線性轉換

9 線性代數在優化中的應用

10 從數據中學習