CS/Math 5486 Advanced Optimization Techniques - Spring 2019

Mon/Wed 2.30 - 3.45 MCB 322


"All is number", Pythagoras

"The purpose of computing is insight, not numbers", R. H. Hamming


Instructor:        Alexey Onufriev
Office:              2160C Torgersen Hall
Phone:              (540)231-4237
Email:               onufriev@cs.vt.edu
Office Hours:    Tue. 2-4 pm
GTA:          Mr. Vartan Abnousi
Office:               ask GTA
Email:               vkesizab@vt.edu
Office Hours: upon request    
Recommended Texts:
Syllabus
Lectures:
  • 1. Introduction with multiple examples.
  • 2. Minimiaztion in multiple dimensions: a motivational intro.
  • 3. Intro Example: local vs. global minima in water models.
  • 4. Intro Example: The grand challenge of computational science: protein folding.
  • 5. Intro to the class project.
  • 6. The beginning: optimization in 1D. Newton's method and its pitfalls.
  • 7. Linear Programming.
  • 8. Intro to ARC and Unix preliminaries.
  • 9. Nedler-Mead (simplex) algorithm.
  • 10. Gradient methods I.
  • 11. Gradient methods II.
  • 11a. On to global optimization. Random Search based on Gradient descent.
  • 12. Technical notes.
  • 13. DIRECT (global optimization)
  • 14. VTDirect (parallel implementation of DIRECT)
  • 15. Constrained optimization
  • 16. Simulated Annealing
  • 17. Genetic Algorithms
  • 18. Sampling in multidimensional space.
    Homework Solution Hints
  • HW1. Solution sketch.

    The Project
  • Brief project info
  • Mid-term progress reports (oral, 15 mins. per report ) due in class: Mon. April 8.
  • Final report: End of semester. See Brief project info for the exact date.
    Computer Resources