CISC 371/3.0 Nonlinear Optimization
Original Author: Randy Ellis
Last Revised: 20190320
Calendar Description
Methods for computational optimization, particularly examining nonlinear functions of vectors. Topics may include: unconstrained optimization; firstorder methods; secondorder methods; convex problems; equality constraints; inequality constraints; applications in machine learning.
Prerequisites:
CISC 271/3.0
Exclusions:
CISC 351/3.0
Learning hours: 120 (36L; 84P)
Degree Planning

This course is required for the
Data Analytics
focus of the COMP degree plan.

This course is a direct prerequisite to:
 CISC 372/3.0 (Advanced Data Analytics)
 CISC 473/3.0 (Deep Learning)
Possible Texts

Amir Beck.
Introduction to Nonlinear Optimization:
Theory, Algorithms, and Applications with MATLAB Algebra (2014).
SIAM Press. ISBN: 9781611973648

S. Boyd and L. Vandenberghe. Convex Optimization (2009).
Cambridge University Press.
ISBN: 9780521833783

E.K.P. Chong and S.H. Zak. An Introduction to Optimization (2011). Wiley.
ISBN: 9780471758006
Topics
 introduction to optimization, optimality conditions (1 week)
 first order methods, gradient descent, backtracking (2 weeks)
 secondorder methods, Newton's method (1 week)
 convex sets, level sets (2 weeks)
 convex optimization, gradient projection (1 week)
 linear constraints and inequality constraints (1 week)
 support vector machines, dual formulations (2 weeks)
 neural networks as problems in optimization (2 weeks)
