Teaching: Using GIT for submission and auto-evaluation of coding exercises
Basic use outside a course context
This lecture aims give an overview and introdution to various approaches to optimization together with practical experience in the exercises. The focus will be on continuous optimization problems and is structured in three parts:
date | topics | slides | exercises (due on 'date'+1) |
Nov 3. | Introduction & Orga | 01-introduction | |
Nov 10. | Unconstrained Optimization | 02-unconstrainedOpt
02-functions |
e00-mathsCheck
e00-pythonCheck |
Nov 17. | Unconstrained Optimization | e01-gradientDescent | |
Nov 24. | Constrained Optimization | 03-constrainedOpt | e02-unconstrainedOpt |
Dec 1. | e03-newtonMethods | ||
Dec 8. | e04-constraints | ||
Dec 15. | Convex Programs | 04-convexProblems | e05-lagrange |
Jan 5. | e06-primaldual | ||
Jan 12. | Differentiable Optimization | 05-differentiableOpt | e07-convexOpt |
Jan 19. | Bound Constraints, Phase I, Restarts | 06-boundsEtc | -cancelled- |
Jan 26. | Global Optimization | 07-globalBayesianOptimization | e08-ILPrelaxation |
Feb 2. | e09-boundsRestarts | ||
Feb 9. | Structure | 08-structure | e10-gpBayesOpt dannysGP.py |
Feb 16. | ExamPreparationExercises |
Basic use outside a course context
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