Math Models of OR, Fall 2017.
MATP4700/ISYE4770
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.
Scores on Homework 1 are now available on LMS.
The mean was 18.6/20 and the median was 19.
 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.
 Information about Exam 1
on September 29.
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.
 Lecture 5: pages 110.
See also Chapter 3 of a new book by Mike Kupferschmid,
which is available as the file imp_chap3.pdf on
LMS.
 Lecture 6: pages 1120.
 Lecture 7: pages 2129.
Also discuss the
the KleeMinty cube.

Duality. Dual simplex. Sensitivity analysis.

Network flows.
 Lecture 15: pages 111.
 Lecture 16: pages 1221.
 Lecture 17: pages 2229.
 Lecture 18: pages 3032.

Integer programming.

Interior point methods.
See also the
typewritten handouts on the
primal affine method and
primaldual methods.
 Lecture 22: pages 1 and 9.
 Lecture 23: pages 28 and 1112.
 Lecture 24: pages 10 and 1324.
See also a handout of a
centering example.

Dynamic programming,
part 1
and
part 2.
 Lecture 25: part 1, pages 1 and 516.
 Lecture 26: part 1, pages 1723, and part 2, pages 13.
 Lecture 27: part 2, pages 4, 5, 813.
 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/the_ampl_book/

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.