CS/Math 5486 Advanced Optimization Techniques - Spring 2025

Mon/Wed 4.00 - 5.15 MCB 233 (zoom when necessary, link on CANVAS)


"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:               alexey@cs.vt.edu
Office Hours:    By request.

Recommended Texts:
Syllabus
Lectures (the specific topics and order may change somewhat):
  • Introduction with multiple examples.
  • Minimiaztion in multiple dimensions: a motivational intro.
  • Intro Example: local vs. global minima in water models.
  • Intro Example: The grand challenge of computational science: protein folding.
  • Intro to the class project(s).
  • The beginning: optimization in 1D. Newton's method and its pitfalls.
  • Intro to ARC and Unix preliminaries.
  • Gradient methods I.
  • Gradient methods II.
  • On to global optimization. Random Search based on Gradient descent.
  • Constrained optimization
  • Nelder-Mead (simplex) algorithm.
  • Simulated Annealing
  • Genetic Algorithms (Differential Evolution)
  • Student Presentations
  • DIRECT (global optimization)
  • VTDirect (parallel implementation of DIRECT)
  • Linear Programming.
  • Sampling in multidimensional space.
  • Notes on Quantum Annealing (with an intro to Q. Computing).
  • Notes on optimization in the context of ML.
    Homework Solution Hints
  • HW1. Solution sketch.

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