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Convex Optimisation

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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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