Instructor:
Dan Gildea office hours MW 45pm
Time: M/W 10:2511:40, CSB 703
Homework
Required text: Boyd and Vanderberghe, Convex Optimization
In addition to this text, we will cover additional numerical
methods taken from Nocedal and Wright and Numerical Recipes. The
second half of the course will focus on applications of
optimization in the context of machine learning.
W 9/3  Intro  Boyd ch 1 
M 9/8  Convex Sets  Boyd ch 2 
W 9/10  Convex Functions  Boyd ch 3 
M 9/15  Convex Optimization Problems  Boyd ch 4 
W 9/17  Convex Optimization Problems 
M 9/22  Duality  Boyd ch 5 
W 9/24  Duality  Boyd ch 5 
M 9/29  Approximation and Statistical Estimation  Boyd ch 6, 7 
W 10/1  Geometric Problems  Boyd ch 8 
M 10/6  Unconstrained Minimization  Boyd ch 9 
W 10/9  Equality Constrained Minimization  Boyd ch 10 
W 10/15  Interior Point Methods  Boyd ch 11 
M 10/20  Interior Point Methods  Boyd ch 11 
W 10/22  BFGS  Nocedal ch 8 
M 10/27  Simulated Annealing  Kirkpatrick 
W 10/29  Dongarra Seminar  
M 11/3  Deterministic Annealing  Rose 
W 11/5  Numerical Methods  Press 10.4  10.6 
M 11/10  Integer Programming  Nemhauser ch II.4 
W 11/12  Shujie Chen  Haider 
M 11/17  Linfeng Song  MERT, MIRA, perceptron, rampion 
W 11/19  Yina Jin  xmeans 
M 11/24  Xiaocheng Peng  spectral learning: Hsu Carreras 
W 11/26  Dual Decomposition for Parsing  Rush et al. Koo et al. 
M 12/1  AdaGrad  Duchi 
W 12/3  Graphical Models  Wainwright and Jordan ch 3 
M 12/8  Graphical Models cont'd  Wainwright and Jordan ch 4 
W 12/10  Project Presentations 
Projects due: 5pm Friday 12/12
Grading
Students will have written assignments, present one lecture,
and complete a final project.
 Homework: 30%
 Lectures and participation: 30%
 Final project: 40%
gildea @ cs rochester edu
December 8, 2014
