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
