Transcript Lecture 1 - Department of Computer Science & Engineering

CSE543T: Algorithms for Nonlinear
Optimization
Yixin Chen
Department of Computer Science & Engineering
Washington University in St Louis
Spring, 2011
Nonlinear Programming (NLP)
problem
Minimize f(x)
(optional) Subject to h(x) =0
g(x) <= 0
Characteristics:
– variable space X: continuous, discrete, mixed,
curves
• Decision variables, control variables, system inputs
– function properties
• Closed form, evaluation process, stochastic, …
Why non-linear?
• Deal or no deal? You can choose
– Take $2M and go home; Or,
– Deal: could get $0.1 or $5M
– What will you choose?
Why study nonlinear optimization?
• Optimization is everywhere
– Constraints are everywhere
– The world is nonlinear (curvature and local optima)
• Plenty of applications
– Investment, networking design, machine learning (NN,
HMM, CRF), data mining, sensors, structural design,
bioinformatics, medical treatment planning, games
• Nonlinear optimization is difficult
– Curse of dimensionality, among many other reasons
Example: Linear Regression
Logistic Regression
• Y=0 or 1
• Where nonlinear functions clearly make
more sense
• P(Y=1) = 1/(1+exp(-wTx))
• Find w to maximize
∏i=1..n P(Yi=1)Yi (1-P(Yi=1))(1-Yi)
SVM
• Minimize
|| w||
• subject to:
yi(w xi - b) ≥ 1
Sensor network optimization
• Variables
– Number of sensors
– Locations of sensors
• Functions
– Minimize the maximum false alarming rate
– Subject to minimum detection probability > 95%
– Highly nonlinear functions
• Based on a Gaussian model and voting procedure
• Computed by a Monte-Carlo simulation
Sodoku
Goals of the course
• Introduce the theory and algorithms for nonlinear
optimization
– One step further to the back of the blackbox
– Able to hack solvers
– But not too much gory details of mathematics
• Hands-on experiences with optimization packages
– Know how to choose solvers
– Understanding of the characteristics of problems/solvers
• Mathematical modeling
– Modeling languages
– CSE applications
• Help solve problems in your own research
Overview of the course
• Unconstrained optimization (6 lectures)
• Convex constrained optimization (3
lectures)
• General constrained optimization (12-14
lectures)
– Penalty methods
– Lagrange methods
– Dual methods
Assignments and grading
• Three or four homework (40%)
• One project (30%)
– In-class presentations
– Reports
• One final exam (30%)