Core Course Notes and Books

APPL/ Math 584 A/B  Course Notes 2024-25
APPL/ Math 581A/B:  Course Notes 2024-25
APPL/ Math 589 A/B:  Request Course Notes 2024-25 

Fall Semester:

• Textbooks for APPL/Math 589A: "Numerical Linear Algebra" by Lloyd N. Trefethen and David Bau, III, SIAM 1997; "Analysis of Numerical Methods" by Eugene Isaccson and Herbert Keller; and "Computer Methods for Ordinary Differential Equations and Differential-Algebraic equations", Uri Ascher and Linda Petzold, SIAM 1998
• Note: To refresh the required background in linear algebra, it is highly recommended to read the first several chapters of "Linear Algebra Done Right" by Axler Sheldon.  As a programming language we will use either Python or Matlab, at the student's choice. Last semester students almost unanimously chose Python; however, there were some who later regretted their choice. I recommend Python to those with at least medium level programming skills and Matlab to people with little programming experience.

Spring Semester:

Optimization:

• Convex Optimization by Stephen Boyd and Lieven Vandenberghe, Cambridge University Press
• Planning Algorithm, S. LaValle
• Calculus of Variations, I. M. Gelfand, S.V. Fomin, Calculus of Variations, Dover
• Optimization Algorithms in Physics, A. K. Hartmann, H. Rieger, Wiley-VCH, 2002
• Introduction to Optimization, B. Polyak. Optimization Software, New York (1987)

Probability, Statistics, Stochastic Processes and Data Science:

• Stochastic Processes in Physics andChemistry, N. van Kampen, third edition ed. Amsterdam: Elsevier, 2007
• Information theory,inference, and learning algorithms, D. J. C. Mackay, Cambridge University Press, 2003.
• The Nature of Computation, C. Moore and S. Mertens, New York, NY, USA: Oxford University Press, Inc., 2011. 
• Document

  An Introduction to Stochastic Modeling

         H. E. Taylor and S. Karlin, 3rd ed. Academic Press, Feb. 1998. 

• Handbook of stochasticmethods for physics, chemistry and the natural sciences, C. W. Gardiner, Berlin: Springer-Verlag, 2004, vol. 13.
• Stochastic Processes: Theory for applications, R.G. Gallager, Cambridge University Press, 2013.