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Notes and course material for MATH50003 Numerical Analysis (2021–2022)

MATH50003NumericalAnalysis (2021–2022)

Notes and course material for MATH50003 Numerical Analysis (2021–2022)

Lecturer: Dr Sheehan Olver

Problem Classes: 2–4pm Thursdays, Huxley 340–342

Overview lecture: 10–11am Fridays, Clore

Q&A: 9:50–10:30 Mondays, 10:40–11:20 Tuesdays on Teams

Course notes

Background material

1. Introduction to Julia: we introduce the basic features of the Julia language.
2. Asymptotics and Computational Cost: we review Big-O, little-o and asymptotic to notation, and their usage in describing computational cost.

Part I: Computing with numbers

1. Numbers: we discuss how computers represent integers and real numbers, as well as their arithmetic operations.
2. Differentiation: we discuss ways of approximating derivatives, including automatic differentiation, which is essential to machine learning.

Part II: Computing with matrices

1. Structured Matrices: we discuss types of structured matrices (permutations, orthogonal matrices, triangular, banded).
2. Decompositions: we discuss algorithms for computing matrix decompositions (QR and PLU decompositions) and their use in solving linear systems.
3. Singular values and condition numbers: we discuss vector and matrix norms, and condition numbers for matrices, and the singular value decomposition.
4. Differential Equations: we discuss the numerical solution of linear differential equations, including both time-dependent ordinary differential equations and boundary value problems, by reduction to linear systems.

Part III: Computing with functions

1. Fourier series: we discuss Fourier series and their usage in numerical computations via the fast Fourier transform.
2. Orthogonal Polynomials: we discuss orthogonal polynomials—polynomials orthogonal with respect to a prescribed weight.
3. Interpolation and Gaussian quadrature: we discuss polynomial interpolation, Gaussian quadrature, and expansions in orthogonal polynomials.

Assessment

1. Practice computer-based exam (Solutions)
2. Computer-based exam (Solutions)
3. Practice final exam (Solutions)
4. Final exam (pen-and-paper): 9 May 2022, 9:00–12:00

Problem sheets

1. Week 1 (Solutions): Binary representation, integers, floating point numbers, and interval arithmetic
2. Week 2 (Solutions): Finite-differences, dual numbers, and Newton iteration
3. Week 3 (Solutions): dense, triangular, banded, permutation, rotation and reflection matrices
4. Week 4 (Solutions): least squares, QR and PLU decompositions
5. Week 5 (Solutions): positive definite matrices, Cholesky, and SVD
6. Week 6 (Solutions): Condition numbers, indefinite integration, and Euler methods
7. Week 7 (Solutions): Two point boundary value problems, convergence, and Fourier series
8. Week 8 (Solutions): Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), and Orthogonal Polynomials.
9. Week 9 (Solutions): Jacobi matrices, classical orthogonal polynomials, and interpolation.
10. Week 10 (Solutions): Orthogonal polynomial roots, interpolatory quadrature, and Gaussian quadrature.

Additional problem sheets:

1. Julia Problem Sheet

Reading List

1. Nicholas J. Higham, Accuracy and Stability of Numerical Algorithms, Chapters 1–3
2. Michael L. Overton, Numerical Computing with IEEE Floating Point Arithmetic, Chapters 2–6
3. Lloyd N. Trefethen & David Bau III, Numerical Linear Algebra, Chapters 1–4
4. Lloyd N. Trefethen, Approximation Theory and Approximation Practice, Chapters 1–4, 17–19
5. The Julia Documentation
6. The Julia–Matlab–Python Cheatsheet

Notes from lectures

1. Integers
2. Floating Point Numbers
3. Floating Point Arithmetic
4. Bounding Errors in Rounding
5. Finite Differences
6. Dual Numbers
7. Structured Matrices
8. Permutations
9. Orthogonal Matrices
10. Least Squares
11. Gram Schmidt and Reduced QR
12. Householder and QR
13. PLU Decomposition
14. Matrix Norms
15. Singular Value Decomposition
16. Cholesky
17. Condition Numbers
18. Indefinite Integration
19. Euler Method
20. Poisson Equation
21. Convergence of Euler Methods
22. Fourier Series
23. Trapezium Rule and Fourier Coefficients
24. Discrete Fourier Transform
25. Fast Fourier Transform
26. Jacobi Matrices
27. Classical Orthogonal Polynomials
28. Polynomial Interpolation
29. Orthogonal Polynomial Roots
30. Interpolatory Quadrature
31. Gaussian Quadrature

What is numerical analysis?

Broadly speaking, numerical analysis is the study of approximating solutions to continuous problems in mathematics, for example, integration, differentiation, and solving differential equations. There are three key topics in numerical analysis:

1. Design of algorithms: discuss the construction of algorithms and their implmentation in software.
2. Convergence of algorithms: proving under which conditions algorithms converge to the true solution, and the rate of convergence.
3. Stability of algorithms: the study of robustness of algorithms to small perturbations in data, for example, those that arise from measurement error, errors if data are themselves computed using algorithms, or simply errors arising from the way computers represent real numbers.

The modern world is built on numerical algorithms:

1. Fast Fourier Transform (FFT): Gives a highly efficient way of computing Fourier coefficients from function samples, and is used in many places, e.g., the mp3 format for compressing audio and JPEG image format. (Though, in a bizarre twist, GIF, a completely uncompressed format, has made a remarkable comeback.)
2. Singular Value Decomposition (SVD): Allows for approximating matrices by those with low rank. This is related to the PageRank algorithm underlying Google.
3. Stochastic Gradient Descent (SGD): Minima of high-dimensional functions can be effectively computed using gradients in a randomised algorithm. This is used in the training of machine learning algorithms.
4. Finite element method (FEM): used to solve many partial differential equations including in aerodynamics and weather prediction. Firedrake is a major project based at Imperial that utilises finite element method.

This is not to say that numerical analysis is only important in applied mathematics. It is playing an increasing important role in pure mathematics with important proofs based on numerical computations:

1. The Kepler conjecture. This 400 year conjecture on optimal sphere packing was finally proved in 2005 by Thomas Hales using numerical linear programming.
2. Numerical verification of the Riemann Hypothesis. It has been proved that there are no zeros of the Riemann zeta function off the critical line provided the imaginary part is less than 30,610,046,000.
3. Steve Smale's 14th problem on the stability of the Lorenz system was solved using interval arithmetic.

Note these proofs are rigorous: as we shall see it is possible to obtain precise error bounds in numerical calculations, and the computations themselves can be computer-verified (a la The Lean Theorem Prover). As computer-verified proofs become increasingly important, the role of numerical analysis in pure mathematics will also increase, as it provides the theory for rigorously controlling errors in computations. Note that there are many computer-assisted proofs that do not fall under numerical analysis because they do not involve errors in computations or approximation or involve discrete problems, for example, the proof the Four Colour Theorem.

The Julia Programming Language

In this course we will use the programming language Julia. This is a modern, compiled, high-level, open-source language developed at MIT. It is becoming increasingly important in high-performance computing and AI, including by Astrazeneca, Moderna and Pfizer in drug development and clinical trial accelleration, IBM for medical diagnosis, MIT for robot locomotion, and elsewhere.

It is ideal for a course on numerical analysis because it both allows fast implementation of algorithms but also has support for fast automatic-differentiation, a feature that is of increasing importance in machine learning. It also is low level enough that we can really understand what the computer is doing. As a bonus, it is easy-to-read and fun to write.

To run Julia in a Jupyter notebook on your own machine:

1. Download Julia v1.7.1
2. Open the Julia app which will launch a new window
3. Install the needed packages by typing (] will change the prompt to a package manager):

] add IJulia Plots ColorBitstring SetRounding

3. Build Jupyter via

] bulid IJulia

4. Launch Jupyter by typing

using IJulia
notebook()