See the book
MATLAB Guide
for more info about MATLAB.
I have separate pages for
CONTEST
and
NESSIE
.
-
MATLAB files
for
Diffusion models for generative Artificial Intelligence: An introduction for applied mathematicians,
C. F. Higham, D. J. Higham, P. Grindrod, SIAM Review, to appear 2024.
MATLAB files
for
Connectivity of Random Geometric Hypergraphs,
Henry-Louis de Kergorlay and Desmond J. Higham,
Entropy (special issue on Higher-Order Networks), 25, 2023
MATLAB files
for
Estimating Network Dimension When the Spectrum Struggles,
by Peter Grindrod, Desmond John Higham, Henry-Louis de Kergorlay,
Royal Society Open Science, 11, 2024
-
MATLAB files
for
A Hierarchy of Network Models Giving Bistability Under Triadic Closure,
by
Stefano Di Giovacchino, Desmond J. Higham, Konstantinos C. Zygalakis,
Multiscale Modeling and Simulation (SIAM), 2022.
MATLAB files
for
Epidemics on hypergraphs: Spectral thresholds for extinction,
by Desmond J. Higham and Henry-Louis de Kergorlay, Proceedings of the Royal Society, Series A, 2021,
and
Mean field analysis of hypergraph contagion models,
by Desmond J. Higham and Henry-Louis de Kergorlay, 2021.
MATLAB files from
A Network Model for Polarization of Political Opinion,
by A. V. Mantzaris and D. J. Higham,
Chaos, 30, 2020.
- R code for
Modelling Burglary in Chicago using a Self-Exciting Point Process with Isotropic Triggering,
European Journal of Applied Mathematics,
by C. Gilmour and D. J. Higham,
to appear.
- R code for
Modelling and Inferring the Triggering Function in a Self-Exciting Point Process,
by C. Gilmour and D. J. Higham,
in Proceedings of Numerical Analysis and Optimization,
Springer,
to appear, 2021.
- MATLAB files from
Deep Learning: An Introduction for Applied Mathematicians,
by C. F. Higham and D. J. Higham,
SIAM Review, 61, 2019, 860--891.
-
netbp.m
from Listing 6.1
-
netbpfull.m
extended version of netbp.m that produces the figure
-
activate.m
from Listing 6.2
-
nlsrun.m
code from section 2 that uses MATLAB's lsqnonlin optimizer
There is also an
extended set of MATLAB files by James Rynn and a
Python version by Alex Hiles.
- MATLAB files from
Modelling and Simulating Chemical Reactions,
by D. J. Higham,
SIAM Review, Education Section, 50, 2008, 347--368.
- MATLAB files from the article:
An Algorithmic Introduction to Numerical Simulation
of Stochastic Differential equations,
by D. J. Higham,
SIAM Review, Education Section,
43, 2001, 525--546.
-
bpath1.m
-
bpath2.m
-
bpath3.m
-
chain.m
(Typo on line 13 has been corrected: ``Xzero2 = 1/sqrt(Xzero)'' becomes
``Xzero2 = sqrt(Xzero)'')
-
em.m
(Typo on line 11 has been corrected: ``dt =1/N'' becomes ``dt = T/N'')
-
emstrong.m
-
emweak.m
(Typo in comment line has been corrected, mu = 1 becomes mu = 0.1.)
-
milstrong.m
(Typo on line 12 has been corrected: ``rand('state',100)'' becomes
``randn('state',100)''. Also
typo in comment on line 33 has been corrected: ``erorrs'' becomes ``errors'')
-
stab.m
-
stint.m
Also note an error in the text. On page 539, line -13,
``whose jth entry'' should be ``whose ith entry''.
Thanks to Joseph P. Skudlarek for spotting these errors.
- MATLAB files from the report:
MAPLE and MATLAB for Stochastic Differential Equations in
Finance,
by D. J. Higham and P. E. Kloeden,
in Programming Languages and Systems in Computational
Economics and Finance, Editor: Soren S. Neilsen,
Kluwer, pages 233--270, 2002.
- MATLAB files from the article
Nine Ways to Implement the Binomial Method for
Option Valuation in MATLAB,
by D. J. Higham,
SIAM Review, Education Section, Vol 44, 2002, 661--677.
(Note that this manuscript and the accompanying files supersede
the original technical report:
Nine Ways to Implement the Binomial Method for Option Valuation in MATLAB,
by D. J. Higham, University of Strathclyde Mathematics Research Report 17 (2001), July 2001.)
- MATLAB files from the article
Black--Scholes for scientific computing students,
by D. J. Higham.
Computing in Science and Engineering (Education Section),
6, 2004, 72--79.
Earlier version was
University of Strathclyde
Mathematics Research Report~01 (2004)
.
- MATLAB files from
LMS Short Course on Computational Differential Equations,
Manchester,
Sept 12-16, 2005,