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CSE543T: Algorithms for Nonlinear Optimization Yixin Chen Department of Computer Science

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Help solve problems in your own research. Overview of the course ... Three or four homework (40%) One project (30%) In-class presentations. Reports ... – PowerPoint PPT presentation

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Title: CSE543T: Algorithms for Nonlinear Optimization Yixin Chen Department of Computer Science


1
CSE543T Algorithms for Nonlinear
OptimizationYixin ChenDepartment of Computer
Science EngineeringWashington University in St
Louis
  • Fall, 2008

2
Nonlinear Programming (NLP) problem
  • Minimize f(x)
  • (optional) Subject to h(x) 0
  • g(x) lt 0
  • Characteristics
  • variable space X continuous, discrete, mixed,
    curves
  • Decision variables, control variables, system
    inputs
  • function properties
  • Closed form, evaluation process, stochastic,

3
Sensor network optimization
  • Variables
  • Number of sensors
  • Locations of sensors
  • Functions
  • Minimize the maximum false alarming rate
  • Subject to minimum detection probability gt 95
  • Highly nonlinear functions
  • Based on a Gaussian model and voting procedure
  • Computed by a Monte-Carlo simulation

4
Sodoku
5
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

6
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

7
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

8
Assignments and grading
  • Three or four homework (40)
  • One project (30)
  • In-class presentations
  • Reports
  • One final exam (30)
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