Introduction to Linear Algebra pdf epub mobi txt 电子书下载 2025

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出版者:Wellesley-Cambridge Press

作者:Gilbert Strang

出品人:

页数:600

译者:

出版时间:2016-8-31

价格:GBP 64.99

装帧:Hardcover

isbn号码:9780980232776

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图书标签:

数学
线性代数
LinearAlgebra
线代
Linear-Algebra
计算机
Strang
教材
线性代数
矩阵
向量
方程组
特征值
行列式
内积空间
线性变换
应用数学
高等数学

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作者简介

Gilbert Strang was an undergraduate at MIT and a Rhodes Scholar at Balliol College, Oxford. His Ph.D. was from UCLA and since then he has taught at MIT. He has been a Sloan Fellow and a Fairchild Scholar and is a Fellow of the American Academy of Arts and Sciences. He is a Professor of Mathematics at MIT, an Honorary Fellow of Balliol College, and a member of the National Academy of Sciences. Professor Strang has published eleven books:

Differential Equations and Linear Algebra (2014)

Introduction to Linear Algebra (1993,1998,2003,2009)

Linear Algebra and Its Applications (1976,1980,1988,2005)

An Analysis of the Finite Element Method, with George Fix (1973, 2008)

Introduction to Applied Mathematics (1986)

Calculus (1991)

Wavelets and Filter Banks, with Truong Nguyen (1996)

Linear Algebra, Geodesy, and GPS, with Kai Borre (1997)

Computational Science and Engineering (2007)

Essays in Linear Algebra (2012)

Algorithms for Global Positioning, with Kai Borre (2012)

He was the President of SIAM during 1999 and 2000, and Chair of the Joint Policy Board for Mathematics. He received the von Neumann Medal of the US Association for Computational Mechanics, and the Henrici Prize for applied analysis. The first Su Buchin Prize from the International Congress of Industrial and Applied Mathematics, and the Haimo Prize from the Mathematical Association of America, were awarded for his contributions to teaching around the world. His home page is math.mit.edu/~gs/ and his video lectures on linear algebra and on computational science and engineering are on ocw.mit.edu (mathematics/18.06 and 18.085).

目录信息

1 Introduction to Vectors
2 Solving Linear Equations
3 Vector Spaces and Subspaces
4 Orthogonality
5 Determinants
6 Eigenvalues and Eigenvectors
7 The Singular Value Decomposition (SVD)
8 Linear Transformations
9 Complex Vectors and Matrices
10 Applications
11 Numerical Linear Algebra
12 Linear Algebra in Probability & Statistics
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