Convex Optimisation

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• Dave Knowles and David Duvenaud
• Thursday 11 November 2010, 14:00-15:30
• Engineering Department, CBL Room 438.

If you have a question about this talk, please contact Shakir Mohamed.

Optimization is a fundamental tool in applied computer science. We aim to give a broad overview of convex optimization with examples relevant to machine learning.

1. What is convexity? BV 3 .1, 3.2
2. Quasiconvexity and unimodality. BV 3 .4
3. Duality, KKT conditions. BV 5 .1-5.3, 5.5.
4. Newton’s method, quadratic convergence. BV 9 .5.
5. Conjugate gradient. NW 5 .1
6. Line search methods, Wolfe conditions. NW 3 .1.
7. Quasi-Newton methods, i.e. BFGS . NW 6.1
8. Interior point methods. BV 11 .2
9. Software: minFunc and CVX

References: BV = Stephen Boyd and Lieven Vandenberghe, Convex Optimization.

Available free here: http://www.stanford.edu/~boyd/cvxbook/ NW = Jorge Nocedal and Stephen Wright, Numerical Optimization, 2006.

This talk is part of the Machine Learning Reading Group @ CUED series.

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