Introduction to Linear Algebra, 6/e (Paperback)
暫譯: 線性代數導論，第6版（平裝本）

Gilbert Strang

Introduction to Linear Algebra, 6/e (Paperback)

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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.

商品描述(中文翻譯)

描述

線性代數在各種定量領域中對於工程師、科學家、經濟學家和商業人士的重要性現在已經與微積分相當或超越。吉爾伯特·斯特朗（Gilbert Strang）在麻省理工學院（MIT）教授線性代數已超過50年，他所開發的課程已成為全球教學的典範。他在MIT OpenCourseWare上的視頻講座已被觀看超過一千萬次，他的十二本教科書在全球讀者中廣受歡迎。



這本斯特朗教授最受歡迎的書籍第六版，線性代數入門，早期介紹了獨立列、矩陣的秩和列空間的概念，以便更積極地開始。然後，書中直接進入線性方程、基本子空間、最小二乘法、特徵值和奇異值等經典主題——在每一種情況下，將關鍵概念表達為矩陣分解。本版的最後幾章探討了優化和從數據中學習：這是當今線性代數最活躍的應用。斯特朗教授以其特有的清晰風格徹底解釋了所有內容，必定會讓下一代學習者感到愉悅和啟發。

目錄大綱

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

目錄大綱(中文翻譯)

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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