Course Notes

ECE 8823 Convex Optimization

These notes were sourced from the following excellent references:- Boyd and Vandenberghe, Convex Optimization,
Cambridge University Press, 2004.

– Luenberger, Optimization by Vector Space Methods, Wiley, 1969.

– Lauritzen, Undergraduate Convexity, World Scientific, 2013.

– Nocedal and Wright, Numerical Optimization, Springer, 1999.

Notes 1, introduction and examples

Notes 2, convex sets

Notes 3, first look at duality

Notes 4, convex functions

Notes 5, second look at duality

Notes 6, gradient descent

see also the notes from L. Vandenberge at UCLA for gradient and Newton

Notes 7, Newton’s method

Notes 8, quasi-newton methods

Notes 9, constrained optimization: geometrical conditions

Notes 10, KKT conditions

Notes 11, the Lagrange dual

Notes 12, examples of duality (maxflow and SVM)

Notes 13, algorithms for constrained optimization

Notes 14, ADMM

Notes 15, distributed ADMM

Notes 16, modeling

Notes 17, convex relaxation

Notes 18, l1 minimization

© 2015 Justin Romberg