Math Models of OR, Fall 2017.
MATP4700/ISYE4770
Recent updates
Contents of this page:
Course basics 
Homeworks 
Exams 
Project 
Notes 
Homework solutions 
Handouts 
Other resources
 Course outline.
 Grades, software, notes, and other material will be posted on
LMS.
 More information on grades,
including an estimated grade after Exam 2.
 Office hours:
Horner, AE316W:
Thursday: 1–2pm, or by appointment.
RPI login for email: horneh
Mitchell, AE325:
Tuesday: 12–2pm, Wednesday: 11am–1pm, or by appointment.
 The books by Ecker and Kupferschmid and by Rardin are
on reserve.
 Homework 1.
A scan of the questions in Chapter 2 of the text can be found on
LMS.
The mean was 18.6/20 and the median was 19.
The solutions to the problems in Chapter 2 are available on
LMS.
 Homework 2.
Scans of the questions in Chapters 3 and 4 of the text can be found on
LMS.
The mean was 18.4/20 and the median was 19.
The solutions to the questions in Chapters 3 and 4 are also available on
LMS
(namely the files chap3a.pdf, chap3b.pdf, chap3c4.pdf).
 Homework 3.
A scan of the questions in Chapter 5 of the text can be found on
LMS.
 Homework 4.
You might find the updated
handout on
strong duality for linear programs helpful
for question 5.17,
especially the last few lines.
You might also find the updated
sensitivity example handout helpful
for question 6.4.
A scan of the questions in Chapter 6 of the text can be found on
LMS.
 Homework 5.
A scan of the questions in Chapter 7 of the text can be found on
LMS.
The solutions to the questions in Chapter 7 are available on LMS.
 Homework 6.
A scan of the questions in Chapter 8 of the text can be found on
LMS.
The solutions to the questions in Chapter 8 are available on LMS.
 Homework 7.
The solutions
are available.
 Homework 8.
A scan of the questions in Chapter 10 of the text can be found on
LMS.
The solutions to the questions in Chapter 10 are available on LMS.
 Information about Exam 1
on September 29,
including the solutions.
You are responsible for the material covered in lectures 1 through 8
(including the material on handling upper bounds in simplex).
No calculators are allowed.
 Information about Exam 2
including the solutions.
 Information about Exam 3
including the solutions.
Scanned copies of my handwritten notes:

Introductory examples. Improving search.
 Lecture 1: pages 14, 6.
Also discuss some
contemporary applications.
 Lecture 2: pages 1012.
 Lecture 4: pages 5, 23, 28, 29.

The
simplex algorithm.
 Lecture 2: pages 19.
 Lecture 3: pages 12 and 1520, 2324.
 Lecture 4: pages 11, 13, 14, and 2122.

More on the simplex algorithm.

Duality. Dual simplex. Sensitivity analysis.

Network flows.

Integer programming.

Interior point methods.

Dynamic programming,
part 1
and
part 2.
 Lecture 25: part 1, pages 14.
 Lecture 26: part 1, pages 516.
 Lecture 27: part 1, pages 1723, and part 2, pages 13, 5, 11.

Lecture 27: part 2, pages 4, 810, 1213.
 Information about AMPL.
 The NEOS Server:
state of the art solvers for numerical optimization.
You can submit your optimization problems written in AMPL
(or other modelling languages)
to this cloud solver.
 The NEOS
optimization guide.

Fourer, Gay, and Kernighan; AMPL: A Modeling Language for Mathematical Programming. The
Scientific Press, Second Edition, 2002. This is the handbook for AMPL and is used for the project.
Available online at http://ampl.com/resources/theamplbook/

Ferris, Mangasarian, and Wright: Linear Programming with MATLAB. SIAM, 2007. Electronic
resource available via the library.

Lee, A First Course in Linear Optimization, Reex Press, 2013–16, available online at
https://github.com/jon77lee/JLee_LinearOptimizationBook/blob/master/LPBook2.95.pdf
 Myths
and counterexamples in optimization. This site shows that you have
to be careful about your assumptions when you state some things that are
"obvious" in linear programming.
 A list of operations research sites.
 RIOT:
Baseball Playoff Races. This site uses linear programming
to determine when a baseball team is eliminated from contention.
John Mitchell's homepage.