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SUMMARY:Convex Optimisation - Dave Knowles and David Duvenaud
DTSTART:20101111T140000Z
DTEND:20101111T153000Z
UID:TALK26206@talks.cam.ac.uk
CONTACT:Shakir Mohamed
DESCRIPTION:Optimization is a fundamental tool in applied computer science
 . We aim to give a broad overview of convex optimization with examples rel
 evant to machine learning.\n# What is convexity? BV 3.1\, 3.2\n# Quasiconv
 exity and unimodality. BV 3.4 \n# Duality\, KKT conditions. BV 5.1-5.3\, 5
 .5.\n# Newton's method\, quadratic convergence. BV 9.5.\n# Conjugate gradi
 ent. NW 5.1\n# Line search methods\, Wolfe conditions. NW 3.1.\n# Quasi-Ne
 wton methods\, i.e. BFGS. NW 6.1 \n# Interior point methods. BV 11.2 \n# S
 oftware: minFunc and CVX\n\nReferences:\nBV = Stephen Boyd and Lieven Vand
 enberghe\, Convex Optimization.\n\nAvailable free here: http://www.stanfor
 d.edu/~boyd/cvxbook/\nNW = Jorge Nocedal and Stephen Wright\, Numerical Op
 timization\, 2006.\n
LOCATION:Engineering Department\, CBL Room 438
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