課程資訊
課程名稱
數值線性代數
Numerical Linear Algebra 
開課學期
105-1 
授課對象
理學院  數學研究所  
授課教師
薛克民 
課號
MATH5411 
課程識別碼
221 U4210 
班次
 
學分
全/半年
半年 
必/選修
選修 
上課時間
星期二8,9(15:30~17:20)星期四5(12:20~13:10) 
上課地點
天數302天數302 
備註
總人數上限:40人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1051MATH5411_ 
課程簡介影片
 
核心能力關聯
本課程尚未建立核心能力關連
課程大綱
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課程概述

This is an introductory graduate level course on numerical linear algebra.
Topics to be covered will include:

1. Basic linear algebra (review)
2. QR factorization/least-squares problems
3. Singular value decomposition (SVD)
4. Conditioning & stability
5. Gaussian elimination, pivoting
6. Eigenvalue problems
7. Iterative methods

Continuation of this course to next semester will be on numerical optimization. 

課程目標
The goal of this course is to provide theoretical insight and to
develop practical skills for solving large scale linear algebra problems
numerically. 
課程要求
Linear Algebra & Introduction to Computational Mathematics 
預期每週課後學習時數
 
Office Hours
每週四 14:00~15:00 
指定閱讀
 
參考書目
1. G. Allaire and S. M. Kaber, Numerical Linear Algebra, Springer 2008. (e-book)
2. J. W. Demmel, Applied Numerical Linear Algebra, SIAM 1997.
3. G. H. Golub and C. F. Van Loan, Matrix Computations, 4rd edition
4. A. Greenbaum, Iterative Methods for Solving Linear Systems, SIAM 1997.
5. L. N. Trefethen and D. Bau, III, Numerical Linear Algebra, SIAM 1997. (e-
book)
6. H. A. van der Vorst, Iterative Methods for Large Linear Systems, 2002. (e-
book)
 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
Week 1
09/15  No class (Mid-Autumn festival)
 
Week 1
09/13  Course overview & examples 
Week 2
09/20  Linear algebra review: Matrices & vectors 
Week 2
09/22  Linear algebra review: Norms 
Week 3
09/29  Linear algebra review: SVD  
Week 3
09/27  No class: Typhoon 
Week 4
10/06  QR factorization: Gram-Schmidt orthogonalization 
Week 4
10/04  Linear algebra review: SVD & four fundamental subspaces 
Week 5
10/13  Least squares problems 
Week 5
10/11  Householder transformation 
Week 6
10/18  Conditioning of linear system problems 
Week 6
10/15  Rank deficient least squares problems
& least-norm solution of undetermined linear system 
Week 7
10/25  Conditioning of eigenvalue problems & linear least squares problems 
Week 7
10/27  Stability of algorithms 
Week 8
11/03  No class (self-learning week) 
Week 8
11/01  Midterm exam 
Week 9
11/10  Stability of algorithm 
Week 9
11/08  No class (self-learning week) 
Week 10
11/17  Cholesky decomposition 
Week 10
11/15  Stability of algorithm, LU decomposition (3hrs makeup class for week 16) 
Week 11
11/24  Givens rotation & Householder reduction to Hessenberg form 
Week 11
11/22  Eigenvalue problems & overview of
eigenvalue algorithms 
Week 12
12/01  QR algorithm without shifts 
Week 12
11/29  Eigenvalue problems: examples,
& Rayleigh quotient & inverse iteration 
Week 13
12/08  Other eigenvalue algorithms ( Jacobi method, bisection,
& divided-and-conquer) 
Week 13
12/06  QR algorithm with shifts 
Week 14
12/15  GMRES (revised) 
Week 14
12/13  Computing SVD, iterative methods: simple iteration, & Arnoldi iteration 
Week 15
12/22  Conjugate gradient algorithm for SPD matrices: Derivation 
Week 15
12/20  Lanczos iteration \& conjugate gradient method 
Week 16
12/29  No class (travel abroad) 
Week 16
12/27  No class (travel abroad) 
Week 17
01/05  Biorthogonalization methods  
Week 17
01/03  Conjugate gradient algorithm: Convergence &
preconditioned conjugate gradient method 
Week 18
01/10  Final exam at Astro/Math 302