Analyse Numérique (ULCO, Fall 2023)
Model of arsenoDNA based on highlevel quantum chemical calculations in PyMol (https://molvisual.chem.ucsb.edu/nucl_struc.html)
As indicated in a note by Lloyd N Trefethen, and contrary to popular belief (according to which the field is often primarily characterized by the study of rounding errors) Numerical analysis should be understood as the study of algorithms for the problems of continuous mathematics (with an emphasis, according to the author, on the word “algorithms”). As general real numbers cannot be exactly represented on computers, the former definition naturally implies that part of the job will then consists in approximating those numbers. Although for some algorithms, such as Gaussian elimination (on which interesting notes can be found here, here and here), the propagation of rounding errors constitutes most of the story,
See here for a list of of 13 (recommended) classical papers in applied mathematics by the same author
In this course, we will discuss direct (including Gaussian elimination) and iterative (including Jacobi and GaussSeidel) methods for the solution of linear systems. We will also cover methods used for the solution of non linear systems (including Newton’s method).
We will introduce the classical theory of polynomial interpolation (including the numerical computation of interpolation polynomials as well as the analysis of the resulting approximation error). We will study interpolation by splines. We will tackle the numerical evaluation of integrals by means of quadrature formulas
Finally we will explore the numerical solution of both Initial value (including Euler’s method) and Boundary value (including the Finite Difference Method) problems
The assignments will include (but not be limited to) paper readings, pen and paper exercises as well as numerical simulations (Julia or Python).
The class will follow the structure
1. Lectures (=CM) (introduction of the new material that will be needed during the lab sessions and for the assignements)
2. Programming (lab) sessions (=TD, TP), (you have the opportunity to apply what you have learned during the lecture, and you can ask all the questions you want to make sure you understand everything before the assignement)
3. Assignments (=DM) (You are given a new problem and you are evaluated on your ability to use the course material to solve this new problem)
Horaire et Salle de cours
Cours et TD/TPs: Mardis: 8h00pm – 10h00, 10h15 – 12h15, 13h1515h15, 15h30 – 17h30 ,
Salle 21A, Bâtiment Angelier, 34 Grande Rue
(voir horaire détaillé cidessous)
Assignments policy
Except if explicitly stated otherwise, assignments are due at the beginning of each class.
Current (temporary) version of the notes: see below as well as the list of sections for the Final
Practice (theory) Questions for each exam can be found by clicking on those exams below
Exams:
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Partiel
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Examen
Partiel : (Matière))
Assignments : 30 % of the grade (Tentative schedule below)
The Github page for the class will be hosted at https://github.com/acosse/NumericalAnalysisCalaisFall2022 and will essentially be used to post numerical verifications for the recitations.
Tentative schedule:
Legend: Lab sessions are in green, Homeworks are in red (right side of the table), dates related to the project are in orange.
Week #  date  Topic  Assignements 
Semaine 1 CM/CM 
20/09 8h0010h00 10h1512h15 
General Introduction Solutions of systems of linear equations (Part I) Direct + Iterative Methods Lecture 1, Recitation 01, Solutions, (HandWritten) Solutions Note sur l’application des mathématiques au Sciences du vivant 

Semaine 2 CM/TD 
27/09 8h0010h00 10h1512h15 
Direct + Iterative Methods for Linear Systems (Part II)
Iterative Methods for Non Linear Systems 
Assignment 1 
Semaine 3 CM/TD 
04/10 8h0010h00 10/1512h15 
Polynomial interpolation + approximation (Part I)
Lecture , Recitation 02, TD 01 
Readings 
Semaine 4 CM/TD 
11/10 13h1515h15 15h3017h30 
Polynomial interpolation + approximation (Part II) Lecture , Recitation 03, (Partial) Solutions recitation 
Readings
Assign. 2, Assig. 1 due 
Semaine 5 CM/TD 
18/10 13h1515h15 15h3017h30 
Laplace Transform Lecture, Recitation 04 
Readings 
Semaine 6 CM/TD 
25/10 8h0010h00 10h1512h15 
Fourier transform Lecture, Recitation 05, Solution 
Assign. 2 due, Project choice MidTerm Revisions 
Semaine 7 CM/TD 
08/11 13h1515h15 15h3017h30  Integration (Part I) Lecture , Recitation 06 
Readings 
Semaine 8 CM/TD/TD 
15/11 8h0010h00 10h1512h15 13h1515h15 
Initial Value problems (Part II) Lecture , Recitation 
Readings 
Semaine 9 CM/TD 
22/11 8h0010h00 10h1512h15 
Boundary value problems + revisions + project presentations  Readings 
Semaine 10 Examen 
30/01 10/1512h15 
Additional ressources (Projects)
Additional ressources (beyond course material)
Lab Sessions and programming policy
The lab sessions will require you to do some programming. It is strongly recommended to use python as it is more flexible and will be useful to you when moving to pytorch later on for more advanced machine learning methods requiring GPU processing.
Downloading and getting started with Julia.