CSE 190 - Discrete and Continuous Optimization
Syllabus
- Linear algebra algorithms
- Nonlinear optimization
- Discrete Optimization
- What is linear programming?
- Linear programming formulations
- Linear programming theory
- Simplex method
- Duality
- Applications
Textbooks
- Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics, Justin Solomon
- A free copy the book is available from Justin Solomon's web page at http://people.csail.mit.edu/jsolomon/
- Understanding and using linear programming, Jiri Matousek and Bernd Gärtner (accessible with UC San Diego credentials)
Problem Sets
- Solving linear programs by hand
- Properties of feasible regions
- Formulating linear programs
- Linear programs in canonical form
- Simplex method
- Duality
Supplementary Reading
- Algorithms by Dasgupta, Papadimitriou, and Vazirani
- Understanding machine learning: From theory to algorithms
- Linear programming solution types
- Perceptron Classifiers (Charles Elkan)
- Understanding and using linear programming, Jiri Matousek and Bernd Gärtner (accessible with UC San Diego credentials)
- Approximation algorithms and semidefinite programming, Bernd Gärtner
- Introduction to linear optimization, Bertsimas, Tsitsiklis, and Tsitsiklis
- Linear and integer programming, Vince Conitzer
- Applied mathematical programming, Bradley, Hax, and Magnanti
- Introduction to optimization: Models and Methods, a course at Harvard University
- Combinatorial optimization, Cook, Cunningham, Pulleyblank, and Schrijver
- Combinatorial algorithms: theory and algorithms, Bernhard Korte and Jens Vygen
- Operations Research Models and Methods, Paul Jensen, Jonathan Bard
- Linear Programming Lecture Notes by Christopher Griffin
- Linear and nonlinear programming, David Luenberger and Yinyu Ye, 4th edition, Springer
- Linear and nonlinear programming, David Luenberger and Yinyu Ye, 3rd edition, Springer
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