Optimization Reading Group

• Location
  • WWH 1314
  • Wednesday 3:30 - 5:00pm
  
  
  
• Lectures
  1. Week 0 (17 Jan): Meet to discuss topic assignment
  2. Week 1 (24 Jan): Line Search Methods -- Sara
     • Wolfe Conditions
     • Steepest Descent
     • Newton's Method
     • Convergence Results
  3. Week 2 (31 Jan): Conjugate Gradients -- Jason
     • Linear CG
     • Nonlinear CG
  4. Week 3 (7 Feb): Quasi Newton Methods -- Marc
     • BFGS
     • Broyden Class
  5. Week 4 (14 Feb): Derivative Free Optimization -- Marc
  6. Week 5 (21 Feb): Theory of Constrained Optimization -- Denis
  7. Week 6 (28 Feb): Linear Programming (Simplex) --Koray
  8. Week 7 (7 Mar): Linear Programming (Interior-Point) --Koray
  9. 14 March, Spring recess
  10. Week 8 (21 Mar): Nonlinear constrained optimization -- Jason
  11. Week 9 (28 mar) IP Methods for Nonlinear Constrained Optimization -- Marc
  12. Week 10 (4 Apr): PDE-constrained Optimization -- Denis
  13. Week 11 (11 Apr): Convex Programming -- Sarah
  14. Week 12 (18 Apr): Integer Programming
  15. Week 13 (25 Apr): Non-smooth optimization --Marc
  
  
  
• Scribe Notes
  Here is a Latex Template file for scribe notes. Please change the filename to lecture#.tex and fill in the proper information for the scribe and topic commands. If you need any shortcuts or commands, let me know. I will eventually create a shortcuts.tex file for everyone.
• Text Book and papers
  
  • Numerical Optimization, by Nocedal and Wright. Some chapters will be here when they become avalable (CIMS password access controlled).
  • Jonathan Richard Shewchuk, An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
  • Forsgren, Gill and Wright, Interior Point Methods for Nonlinear Optimization, SIAM Review
  • Todd, Semidefinite Optimization , Acta Numerica
  • Alizadeh, Haeberly and Overton, Primal-Dual Interior-Point Methods for Semidefinite Programming:Convergence Rates, Stability and Numerical Results , SIAM J. Optim
  • Burke, Lewis, and Overton, A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization , SIAM J. Optim
  
  
  (text below here is stolen shamelessly from Prof. Overton's nonlinear optimization course page)
  
• Software
  For experimenting with our own optimization programs, Matlab is a good choice. Matlab is an excellent environment for small-scale numerical computing. However, its Optimization Toolbox is not very good. See here for an excellent up-to-date guide to optimization software. Optimization problems can be submitted over the web to NEOS. Another great resource is the modeling language AMPL.
  
  Matlab is a product of The MathWorks. You can order your own copy of Matlab for $99 or you can use Matlab on the Courant Sparcstation network (or dial in from home). For Matlab documentation, type "helpdesk" at the Matlab prompt. To get started, try out A Free Matlab Online Tutorial or Another Tuturial or look for others by a web search. You may want to look at a very outdated but still useful Introductory Matlab Primer (3rd and last edition, postscript file). There are many books on Matlab; I recommend Matlab Guide, by Higham and Higham, but you will find many other resources on the web, including the latest information on Matlab 7.0.
  
  
• SIAM
  As an NYU graduate student you have the opportunity to join SIAM for free. SIAM is the main professional organization for applied and computational math, and offers a number of benefits to members. I've been a member since I was a graduate student, and have benefitted in many ways from my association with SIAM.