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EP30.FSF1106 — Advanced Numerical Analysis | Chương trình đào tạo NEU

EP30.FSF1106 — Advanced Numerical Analysis

Credits
3
Self-study hours
90
Showing intake
K68-2026

Course description

This course provides an advanced and rigorous study of computational methods used to approximate solutions to mathematical problems in science and engineering. The course develops the theoretical foundations and practical implementations of:

  • Direct and iterative methods for solving large linear systems
  • Approximation and orthogonal polynomial methods
  • Eigenvalue algorithms
  • Nonlinear system solvers
  • Numerical methods for ODE boundary-value problems
  • Numerical methods for elliptic, parabolic, and hyperbolic PDEs

Students will build strong analytical skills (error, stability, convergence analysis) and computational skills (MATLAB/Python implementation). Through weekly labs and a final project, students learn reproducible scientific computing and numerical experimentation.

Course requirements

Requirements for taking final /terminal exams

Students are allowed to take the final exam/terminal exams (50%) if their attendance score (10%) reaches 5 points or more (on the scale of 10).

Requirements for attending classes

Students are responsible for attending all classes. In case of absence from school due to force majeure reasons, sufficient and reasonable proofs must be provided. For each absence, 1 point will be deducted 1 point. Students who miss any classes more than 3 times, with or without reason, will be considered as failing to complete the course and have to re-register.

Students will be awarded points for constructive comments these points are added to attendance points and group assignments.

Groups who do not submit the group work will receive a score of 0 (zero). Late submissions will be deducted for each day of late submission.

Requirements for in-class behaviour

The course is conducted on the principle of respecting learners and lecturers. Any behaviour that affects the teaching and learning process is strictly prohibited.

Students must come to class on time. Students who are late more than 10 minutes after class starts will not be allowed to attend the class. Do not make noise and affect others during the learning process.

Laptops and tablets are only used for the purpose of taking notes and calculating for lectures and exercises, absolutely forbidden to use for other purposes.

Detailed content

The course content  is organized into seven chapters: 

  • Chapter 1: Direct Methods for Solving Linear Systems
  • Chapter 2: Iterative Techniques in Matrix Algebra
  • Chapter 3. Approximation Theory
  • Chapter 4. Approximating Eigenvalues
  • Chapter 5. Numerical Solutions of Nonlinear Systems of Equations
  • Chapter 6. Boundary-Value Problems for Ordinary Differential Equations
  • Chapter 7. Numerical Solutions to Partial Differential Equations

Lecturers

  • Vuong Van Yen
  • Le Thi Anh
  • Phung Minh Duc
  • Bui Quoc Hoan
  • Nguyen Duc Nam
  • c. Nguyễn Mai Quyên
  • c. Hoàng Văn Thắng
  • Doan Trong Tuyen
  • Tong Thanh Trung

Teaching plan

Week 1

Course Introduction & Numerical Foundations Topics: Overview of numerical analysis; floating-point arithmetic Error types (round-off, truncation), conditioning, stability Review of vector/matrix norms
References
Syllabus [1] Chapter 1-5
Activities
 Lecture + whiteboard examples  Lab: MATLAB/Python intro; computing norms & floating-point rounding  Discussion: Why conditioning matters
Assessment
 Quiz 1 (Basic error analysis)  Lab Exercise 1 submission

Week 2

Chapter 1: Direct Methods for Solving Linear Systems Linear Systems of Equations Pivoting (Chapter 6.1 – 6.2) Topics: Gaussian elimination Pivoting strategies & numerical stability
References
[1] Chapter 6 [2] Chapter 3 [3] Chapter 3
Activities
 Lecture + worked examples  Lab: Implement GE + partial pivoting  Case Study: Hilbert matrix conditioning
Assessment
 Homework 1 (GE + pivoting)  Quiz 2

Week 3

Chapter 1: Direct Methods for Solving Linear Systems (continue) Matrix Inversion Determinants (Chapter 6.3 – 6.4) Topics: Numerical matrix inversion Determinants and numerical relevance
References
[1] Chapter 6 [2] Chapter 3 [3] Chapter 3

Learning resources

  • Numerical Analysis — Richard L. Burden and J. Douglas Faires, Ninth Edition, 2011
  • Introduction to Numerical Analysis — J. Stoer, Bulirsch, Springer, 1999
  • Numerical methods: design — Greenbaum, T.P. Chartier, analysis and computer implementation of algorithms, 2012
Activities
 Lecture  Lab: Compare inverse-based vs factorization-based solvers  Discussion: Why “avoid computing inverse”
Assessment
 Lab Exercise 2 (LU decomposition)  Short written reflection

Week 4

Chapter 1: Direct Methods for Solving Linear Systems (continue) Matrix Factorization (Chapter 6.5) Topics: LU decomposition Cholesky factorization Forward/backward substitution
References
[1] Chapter 6 [2] Chapter 3 [3] Chapter 3, 5
Activities
 Lecture  Lab: Implement LU, test on SPD vs non-SPD matrices  Peer review: code efficiency and readability
Assessment
Homework 2 (LU/Cholesky tasks)

Week 5

Chapter 1: Direct Methods for Solving Linear Systems (continue) Special Types of Matrices Software Survey (Chapter 6.6 – 6.7) Topics: Banded, sparse, symmetric, SPD matrices Practical solvers (MATLAB backslash, SciPy’s sparse solvers)
References
[1] Chapter 6 [2] Chapter 3 [3] Chapter 5
Activities
 Lecture  Lab: Solve sparse systems; explore library behavior  Mini demo: storage savings for banded matrices
Assessment
 Quiz 3  Lab Exercise 3

Week 6

Chapter 2: Iterative Techniques in Matrix Algebra 2.1. Norms of vectors and Matrices 2.2. Eigenvalues and Eigenvector & Stability Concepts (Chapter 7.1 – 7.2[1]) Topics: Vector and matrix norms Spectral radius Eigenvalues in convergence/stability analysis
References
[1] Chapter 7 [2] Chapter 2, 6 [3] Chapter 4
Activities
 Lecture  Lab: Compute norms, estimate spectral radius  Whiteboard exercise: convergence criteria
Assessment
Homework 3 (Iterative vs Direct comparison)

Week 7

Chapter 2: Iterative Techniques in Matrix Algebra (continue) 2.3. Jacobi, Gauss–Seidel Techniques 2.4. Relaxation Techniques for Solving Linear Systems (Chapter 7.3 – 7.4 [1]) Topics: Jacobi & Gauss–Seidel iterations SOR acceleration Convergence behavior
References
[1] Chapter 7 [2] Chapter 7 [3] Chapter 4
Activities
 Lecture  Lab: Implement all three methods, compare convergence rates  Collaborative experiment: effect of ω in SOR
Assessment
 Quiz 4  Lab Exercise 4

Week 8

Chapter 2: Iterative Techniques in Matrix Algebra (continue) 2.5. Error Bounds, Iterative Refinement 2.6. Conjugate Gradient (Chapter 7.5 – 7.6 – 7.7 [1]) Topics: Error bounds and residuals Iterative refinement Conjugate Gradient on SPD matrices
References
[1] Chapter 7 [2] Chapter 7 [3] Chapter 4
Activities
 Lecture  Lab: Implement CG for large sparse Poisson matrices  Discussion: Preconditioning introduction
Assessment
 Midterm Exam (Covers Weeks 1–7)  Homework 4

Week 9

Chapter 3. Approximation Theory 3.1. Discrete Least Squares Approximation 3.2. Orthogonal Polynomials and Least Squares Approximation (Chapter 8.1 – 8.2 [1]) Topics: Discrete least squares Orthogonal polynomials (Legendre, etc.) Approximation error
References
[1] Chapter 8 [2] Chapter 8 [3] Chapter 6
Activities
 Lecture  Lab: Fit polynomial models; analyze residuals  Group reasoning: Why orthogonality matters
Assessment
 Quiz 5  Lab Exercise 5

Week 10

Chapter 3. Approximation Theory 3.3. Chebyshev Polynomials and Least Squares Approximation 3.4. Rational Function Approximation Approximation 3.5. Trigonometry Polynomial Approximation 3.6. Fasr Fourier Transforms (Chapter 8.3 – 8.4 – 8.5 – 8.6 [1]) Topics: Chebyshev polynomials & minimax approximation Rational approximation (Padé) Trigonometric polynomial approximation Fast Fourier Transform (FFT)
References
[1] Chapter 8 [2] Chapter 8 [3] Chapter 6
Activities
 Lecture  Lab: Chebyshev approximation + FFT demos  Experiment: aliasing phenomenon
Assessment
Homework 5

Week 11

Chapter 4. Approximating Eigenvalues 4.1. Linear Algebra and Eigenvalues 4.2. Orthogonal Matrices and Similarity Transformations 4.3. The Power Method 4.4. Householder’s Method 4.5. The QR Algorithm 4.6. Singular Value Decomposition (Chapter 9.1 – 9.6 [1]) Topics: Power method Householder reduction QR algorithm Singular Value Decomposition
References
[1] Chapter 9 [2] Chapter 6 [3] Chapter 7
Activities
 Lecture  Lab: Implement Power Method; compare with MATLAB eig  Mini project workshop: PCA using SVD
Assessment
 Quiz 6  Lab Exercise 6

Week 12

Chapter 5. Numerical Solutions of Nonlinear Systems of Equations 5.1. Fixed Points for Functions of Several Variables 5.2. Newton’s Methods 5.3. Quasi-Newton Methods 5.4. Steepest Descent Techniques 5.5. Homotopy and Continuation Methods (Chapter 10.1 – 10.5[1]) Topics: Multivariate Newton’s method Quasi-Newton (Broyden) Steepest descent Continuation methods (conceptual)
References
[1] Chapter 10 [2] Chapter 5 [3] Chapter 8
Activities
 Lecture  Lab: Solve nonlinear systems numerically  Discussion: When Newton's method diverges
Assessment
Homework 6

Week 13

Chapter 6. Boundary-Value Problems for Ordinary Differential Equations 6.1. The Linear Shooting Method 6.2. The Shooting Method for Nonlinear Problems 6.3. Finite-Difference Methods for Linear Problems 6.4. Finite-Difference Methods for Nonlinear Problems 6.5. The Rayleigh-Ritz Method (Chapter 11.1 – 11.6[1]) Topics: Linear + nonlinear shooting methods Finite-difference approximations Rayleigh–Ritz method
References
[1] Chapter 11 [2] Chapter 12 [3] Chapter 9
Activities
 Lecture  Lab: Solve BVP using shooting & finite differences  Experiment: mesh refinement and error
Assessment
 Quiz 7  Lab Exercise 7

Week 14

Chapter 7. Numerical Solutions to Partial Differential Equations 7.1. Elliptic Partial Differential Equations 7.2. Parabolic Partial Differential Equations 7.3. Hyperbolic Partial Differential Equations (Chapter 12.1 – 12.3[1]) Topics: Elliptic (Laplace) equations Parabolic (Heat) equations Hyperbolic (Wave) equations Stability: Von Neumann, CFL condition
References
[1] Chapter 12 [2] Chapter 13 [3] Chapter 10
Activities
 Lecture  Lab: Explicit vs implicit heat equation methods  Derive CFL condition in class
Assessment
 Homework 7  Final Project (code + experiments) – Draft due

Week 15

7.4. Introduction to Finite Elements & Course Synthesis (Chapter 12.4 – 12.5[1]) Topics: Intro to Finite Element Method (FEM) Variational formulation concepts Survey of PDE software (FEniCS, MATLAB PDE Toolbox)
References
[1] Chapter 12 [2] Chapter 14 [3] Chapter 11
Activities
 Lecture  Lab: FEM demo (given starter code)  Course wrap-up: connections between all methods
Assessment
 Final Project Presentation  Final Report Submission

Week 15

Review
References
[1] Chapters 6-12, [2] Chapters: 1, 2, 3, 5, 6, 7, 12, 13, 14 [3] Chapters 1- 11
Activities
Lecturer: + Announce the results of the process assessment + Troubleshooting + Summary of the main content of the course + Study guide and final exam Students ask questions and discuss
Assessment
Criteria: Level of participation; Level of interaction; Quality of answers. Tools: Rubric 1

Week 16

Final exam
Activities
Students take exams according to the given schedule
Assessment
Criteria: According to the final exam requirement Tools: Rubric 2