Discrete and Continuous Optimization

Fall 2016

People

Professor: Ramamohan Paturi
Room: CSE 4246
Email: paturi@cs.ucsd.edu
Teaching Assistant:
Email:

Syllabus, evaluation, and hours

Syllabus
Evaluation and hours

Weekly Activity

Homework Assignments

For each homework, solve the listed problems. Consult the style guide for writing solutions.

Academic Honesy

Each student is expected to work by herself/himself to complete homework, quizzes and other exams. The authorized materials for this course include the course text book, lecture notes, solutions and other handouts provided by the instructor or TA, and textbooks and lecture notes from the prerequisite UC San Diego courses. Use of any other materials requires an explicit approval from the instructor. Here is the detailed policy on Integrity of Scholarship Agreement.

Resources

1. Guidelines for writing solutions
2. Problems and solutions
3. Sample tests
4. Moodle
   
5. Algorithmic problem solving
6. Mathematics refreshers
   • Logarithms (Thanks to the University of Wollongong, Australia)
   • Another refresher on logarithms (Thanks to Khan Academy)
   • Cornell University Math Support Center Capsule: Logarithms
   • Binary number system YouTube video by dizauvi
   • Floor and ceiling functions(Thanks to Greg Gamble)
   • Induction, sequences, geometric series, ... (Thanks to Khan Academy)
   • Mathematics for Computer Science by Lehman, Leighton and Meyer
7. Books and lecture notes
   1. An introduction to algorithms for continuous optimization by Nicholas Gould
   2. Modern optimization with R, Paulo Cortez
   3. Discrete optimization, R. Gary Parker
   4. Graphs, algorithms, and optimization, William Kocay and Donald L. Kreher
   5. Integer and Combinatorial optimization, Laurence A. Wolsey, George L. Nemhauser
   6. Integer programming, Michele Conforti, Gerard Cornujelos, Giacomo Zambelli
   7. Convex optimizaiton, Gregory Boyd and Lieven Vandenberghe
   8. Undergraduate convexity, Niels Lauritzen
   9. A course in convexity, Alexander Barvinok
   10. Understanding and using linear programming, Jiri Matousek and Bernd Gärtner
   11. Approximation algorithms and semidefinite programming, Bernd Gärtner
   12. Introduction to linear optimization, Bertsimas, Tsitsiklis, and Tsitsiklis
   13. Linear and integer programming, Vince Conitzer
   14. Applied mathematical programming, Bradley, Hax, and Magnanti
   15. Introduction to optimization: Models and Methods, a course at Harvard University
   16. Combinatorial optimization, Cook, Cunningham, Pulleyblank, and Schrijver
   17. Combinatorial algorithms: theory and algorithms, Bernhard Korte and Jens Vygen
   18. Operations Research Models and Methods, Paul Jensen, Jonathan Bard