Fall 2014  STOR 612
Deterministic models in OR
Instructor: Gabor Pataki.
TA: Tianxiao Sun
Class : Tue, Thu 5.006.15 PM, Hanes 107
Office hours :
For Gabor: Tuesday 3.454.45 Pm, Hanes 307.
Usually I am also available after class.
For Tianxiao: Wed 2:003:30 pm
Questions?
Exams

First midterm: Oct 2, Thu, during classtime, Hanes 107. In class, closed book, closed notes. Subject: Only LP formulations.
Exam 1 in 2012 with solutions.
Exam 1 in 2013 with solutions.
Exam 1 in 2014.
Exam 1 in 2014 with partial solutions.

Second midterm: Nov 13, Thursday, in class time. I will give some extra time at the end.
In class, closed book, closed notes. Subject: shortest paths, and dynamic programming.
We call Category 1 students: PhD students in OR, Stat, INSTORE, KFBS, Math, CS.
Everyone else is Category 2.
The exam will be different for Cat1 and Cat2 students.
Cat1 students need to remember and reproduce algorithms exactly as covered in class, and reproduce their correctness proofs.
Cat 2 students will need to only state the algorithms, and theorems that were covered in class, but not the proof of their correctness. However: Cat 2 students will still need to **invent** some algorithms, and prove correctness.
Eg. Cat 1 students may be asked to state Dijkstra's algorithm, and prove its correctness. Cat 2 students may be asked only to state Dijkstra's algorithm.
Still, it is strongly advised for cat 2 students to study, and absorb all algorithms, and their proofs.
Some questions on the exam may be about some small modifications of these algorithms.
Exam 2 from 2012 with solutions
Exam 2 in 2013.
Exam 2 in 2013 with solutions.
Exam 2 in 2014.
Exam 2 in 2014 with solutions.

Final: Tuesday, December 9, 47 PM. In class. The final exam is cumulative.
It is on the same model as exam 2, (different for MS/undergrad, and PhD, etc.)
Final in 2013 with partial solutions
Announcements

aug 20. Syllabus and homework policies (pdf file)
 aug 20. HW is due BEFORE the beginning of class, or it can be placed in the instructor's
mailbox before the beginning of the class. Late hws cannot be accepted.
 aug 20.
How to submit homeworks with an AMPL ingredient: you need to
 Submit a handwritten solution explaining the LP model as usual, and in addition

a printout of your files, and of the run, and

send an email containing the model and data files to the TA, Tianxiao Sun,
whose address is "tianxias" "at" "live" "dot" "unc.edu".
As part of the grading, he will run all of them to check their correctness.
 aug 20.
Generalities about AMPL hws:
I usually give the optimal solution value, so you can check your work.
If you get an objective value different from the one given, then it is very likely
that your model is incorrect. The data was usually made up in a way, so that incorrect models
will give a solution value different from the correct model's.
Linear Programming handouts
Networks and DP handouts
MIP handouts
AMPL handouts
Matlab handouts
Homeworks and solutions