Difference between revisions of "Slides from Lectures and Tutorials"
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** [[Media:15-largeNLP.pdf|Active-Set Methods for Large-Scale NLP]] | ** [[Media:15-largeNLP.pdf|Active-Set Methods for Large-Scale NLP]] | ||
** [[Media:08-tutorial.pdf|Tutorial 8]] | ** [[Media:08-tutorial.pdf|Tutorial 8]] | ||
| − | *** Model | + | *** Model and run files for 1D UFL optimization [[Media:UFL.mod|UFL.mod]], [[Media:UFL.ampl|UFL.ampl]], and a data file for multi-dimensional UFL [[Media:UFL1.dat|UFL1.dat]] (you need to write your own model file) |
Revision as of 22:45, 19 September 2016
- Day 1: Introduction and Applications of Optimization
- Day 2: Case Study and Methods for Unconstrained Optimization
- Day 3: Newton and Conjugate Gradient Methods
- Day 4: Trust-Region Methods and Bound Constrained Optimization
- Restricted Step Methods
- Methods for Bound Constrained Optimization
- Tutorial 4 new version of the Matlab routines Zip archive with Matlab routines you can modify
- Day 5: Optimality Conditions, Convexity, and Duality
- Day 6: Linear and Quadratic Programming
- Linear Programming
- Quadratic Programming
- Tutorial 6
- Data file for transshipment model network.dat
- Data file for portfolio model portfolio.dat
- Part of Student.mod model file part.mod
- Day 7: Nonlinear Programming Methods Part I
- Sequential Quadratic Programming
- Interior-Point and Augmented Lagrangian Methods
- Tutorial 7
- Data files for sparse optimization SparseOpt.dat, lasso6_20_2.dat, lasso20_100_14.dat
- Day 8: Nonlinear Programming Methods Part II