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Spring, 2016

CSE543T: Algorithms for Nonlinear Optimization Yixin Chen, PhD Professor Department of Computer Science & Engineering Washington University in St Louis. Spring, 2016. Nonlinear Programming (NLP) problem. Minimize f(x) (optional) Subject to h(x) =0 g(x) <= 0

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Spring, 2016

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  1. CSE543T: Algorithms for Nonlinear OptimizationYixin Chen, PhDProfessorDepartment of Computer Science & EngineeringWashington University in St Louis Spring, 2016

  2. 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, …

  3. 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?

  4. 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

  5. Example: Linear Regression

  6. 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)

  7. SVM • Minimize • || w|| • subject to: • yi(w xi - b) ≥ 1

  8. 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

  9. Sodoku

  10. 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

  11. 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

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

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