Computer Science 577

Seminar in AI: Optimization

Fall 2014

Instructor: Dan Gildea office hours MW 4-5pm
Time: M/W 10:25-11:40, CSB 703


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 IntroBoyd ch 1
M 9/8 Convex SetsBoyd ch 2
W 9/10 Convex FunctionsBoyd ch 3
M 9/15 Convex Optimization ProblemsBoyd ch 4
W 9/17 Convex Optimization Problems
M 9/22 DualityBoyd ch 5
W 9/24 DualityBoyd ch 5
M 9/29 Approximation and Statistical EstimationBoyd ch 6, 7
W 10/1 Geometric ProblemsBoyd ch 8
M 10/6 Unconstrained MinimizationBoyd ch 9
W 10/9 Equality Constrained MinimizationBoyd ch 10
W 10/15Interior Point MethodsBoyd ch 11
M 10/20Interior Point MethodsBoyd ch 11
W 10/22BFGSNocedal ch 8
M 10/27Simulated AnnealingKirkpatrick
W 10/29Dongarra Seminar
M 11/3Deterministic AnnealingRose
W 11/5 Numerical MethodsPress 10.4 - 10.6
M 11/10 Integer ProgrammingNemhauser ch II.4
W 11/12Shujie ChenHaider
M 11/17Linfeng SongMERT, MIRA, perceptron, rampion
W 11/19Yina Jinx-means
M 11/24Xiaocheng Pengspectral learning: Hsu Carreras
W 11/26Dual Decomposition for ParsingRush et al. Koo et al.
M 12/1AdaGradDuchi
W 12/3Graphical ModelsWainwright and Jordan ch 3
M 12/8 Graphical Models cont'dWainwright and Jordan ch 4
W 12/10 Project Presentations

Projects due: 5pm Friday 12/12


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