Courant Institute, NYU
HONORS NUMERICAL ANALYSIS (MATH 396)
Spring 2022
Instructor
Mike O'Neil (oneil@cims.nyu.edu)

Office hours
By appointment

Description
“Numerical analysis is the study of algorithms for the problems of continuous mathematics” - L. N. Trefethen, 1992. This course will cover the analysis of numerical algorithms which are ubiquitously used to solve problems throughout mathematics, physics, engineering, finance, and the life sciences. In particular, we will analyze algorithms for solving nonlinear equations; optimization; finding eigenvalues/eigenvectors of matrices; computing matrix factorizations and performing linear regressions; function interpolation, approximation, and integration; basic signal processing using the Fast Fourier Transform; Monte Carlo simulation. View the syllabus for more detailed information.

Lecture
Monday & Wednesday 2:00pm - 3:15pm, CIWW 512

Recitation
Tristan Goodwill (tg1644@nyu.edu)
Friday 12:30pm - 1:45pm, CIWW 512
Materials
The following textbooks are recommended for reference material throughout the course:
- Burden, Faires, and Burden, Numerical Analysis, Cengage, 2015
- Greenbaum and Chartier, Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms, Princeton, 2012
- Suli and Mayers, An Introduction to Numerical Analysis, Cambridge, 2003
- Driscoll and Braun, Fundamentals of Numerical Computation, SIAM, 2017

Grading
The overall course grade will be determined from a final numerical weighted average. The following breakdown will be used to compute an overall numerical grade:
- 30% Homework (6 assignments, 5% each)
- 30% Midterm
- 40% Final exam (cumulative)

Announcements
None.
Schedule
Below is an updated list of discussion topics along with any documents that were distributed, notes, or relevant code.
Date Topics Materials
Jan 24 Overview, Julia Notes
Julia: A Fresh Approach to Numerical Computing
Recording
Jan 26 IEEE floating-point arithmetic Notes
macheps.jl
Driscoll & Braun, Floating-point numbers
Overton, 2001
Jan 31 Bisection, secant, and Newton's method Suli & Mayers: 1.4 - 1.6
Notes
Feb 2 Convergence of Newton's method,
rates of convergence, nonlinear systems
Suli & Mayers: 1.4, 1.7
Notes
Feb 7 Vector and matrix norms, conditioning Suli & Mayers: 2.7
Driscoll & Braun: 2.7
Notes
Feb 9 Matrix conditioning, multivariate Newton Driscoll & Braun: 2.8
Driscoll & Braun: 4.5
Notes
Feb 14 Optimization Driscoll & Braun: 4.6
Notes
Feb 16 Gaussian elimination, LU
Operation counts
Driscoll & Braun: 2.2
Driscoll & Braun: 2.3
Driscoll & Braun: 2.4
Driscoll & Braun: 2.5
Notes
Feb 21 NO CLASS - PRESIDENTS' DAY
Feb 23 Cholesky, pivoted LU,
Backward stability, Gram-Schmidt
Driscoll & Braun: 2.6
Driscoll & Braun: 2.8
Driscoll & Braun: 2.9
Driscoll & Braun: 3.3
Notes
Feb 28 Modified Gram-Schmidt,
Householder reflections
Driscoll & Braun: 3.3
Driscoll & Braun: 3.4
Trefethen & Bau: Lecture 6-11 (More advanced)
Notes
Mar 2 Householder reflections, linear regression,
singular value decomposition
Driscoll & Braun: 7.3
Notes
Mar 7 Power method, with shift Suli & Mayers: 5.4, 5.8
Notes
Mar 9 Midterm Exam
Mar 14 NO CLASS - SPRING BREAK
Mar 16 NO CLASS - SPRING BREAK
Mar 21 Inverse power method with shift,
Jacobi's method
Suli & Mayers: 5.8, 5.3
Notes
Mar 23 Convergence of Jacobi's method,
QR algorithm
Suli & Mayers: 5.3, 5.5-5.7.2
QR Algorithm
Notes
Mar 28 Numerically computing the SVD
Lagrange interpolation
Suli & Mayers: 6.1-6.2
Notes
Mar 30 Barycentric Lagrange interpolation Suli & Mayers: 6.1-6.3
Notes
Apr 4 Minimax approximation, Chebyshev polynomials Suli & Mayers: Ch 8
Notes
Apr 6 Function approximation in the 2-norm Suli & Mayers: Ch 9
Notes
Apr 11 Orthgonal polynomials, trapezoidal rule Suli & Mayers: 7.1-7.4
Notes
Apr 13 Composite integration rules,
Euler-Maclaurin formula
Clenshaw-Curtis quadrature
Suli & Mayers: 7.5-7.6
Notes
Apr 18 Richardson extrapolation
Gaussian quadrature
Notes
Apr 20 NO CLASS
Apr 25 Intro to Fourier series
Discrete Fourier analysis
Briggs & Henson: Ch 1, 2.1-2.5
Apr 27 The Discrete Fourier Transform
Inverse transform, convolutions
Briggs & Henson: Ch 2.1-2.6, 3.1-3.2
May 2 The Fast Fourier Transform
Spectral differentiation, integration
Briggs & Henson: Ch 10.1-10.2, 10.4, 10.6
May 4 Clenshaw-Curtis quadrature (revisited)
Discrete cosine transform
May 9 Random number generation
May 13 Final Exam 2:00pm - 3:50pm