DS211: Numerical Optimization

Department of Computational and Data Sciences

Numerical Optimization

  • Instructor: Deepak Subramani (www | email)
  • Teaching Assistant:
  • Course number: DS211
  • Credits: 3:0
  • Semester: Aug, 2019
  • Lecture: Tue/Th 11:30AM-1PM (First class: Aug 6, 11:30AM)
  • Room: CDS 202
  • Course Sign-up: https://forms.gle/MKj9p24AVsAj7u3G6


Introduces numerical optimization with emphasis on convergence and numerical analysis of algorithms as well as applying them in problems of practical interest. Topics include: Methods for solving matrix problems and linear systems that arise in the context of optimization algorithms. Major algorithms in unconstrained optimization (e.g., modified Newton, quasi-Newton, steepest descent, nonlinear conjugate gradient, trust-region methods, line search methods), constrained optimization (e.g., simplex, barrier, penalty, sequential gradient, augmented Lagrangian, sequential linear constrained, interior point methods), derivative-free methods (e.g., simulated annealing, Bayesian optimization, Surrogate-assisted optimization), dynamic programming, and optimal control.


Basic knowledge of Numerical Methods, linear algebra, and consent from the advisor

Text Books

*Practical Optimization by Philip E. Gill, Walter Murray, Margaret H. Wright, Emerald Group Publishing Limited (1982).

*Numerical Optimization, J. Nocedal and S. Wright, Springer Series in Operations Research and Financial Engineering, 2006.

*Practical Methods of Optimization by R. Fletcher 2nd edition, Wiley, 1987.

*Introduction to Linear Optimization by Bertsimas, Tsitsiklis. MIT Press (1997)

*Linear Programming with MATLAB, M. Ferris, O. Mangasarian, and S. Wright, MPS-SIAM Series on Optimization, 2007.


This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.

Find our class page at: https://piazza.com/iisc.ernet.in/fall2019/ds211/home


  1.   Syllabus, Schedule and Grading Scheme
  2.  Class 1 notes posted on piazza