I.E. 2081: NONLINEAR OPTIMIZATION
INSTRUCTOR:
Prof.
Jayant Rajgopal
1032 Benedum Hall
Tel. No.: 412-624-9840
e-mail: rajgopal@pitt.edu
PREREQUISITES:
- Differential calculus
- Vectors, matrices and linear algebra
- Familiarity with basic concepts from
real analysis such as sets, functions, continuity, etc.
- An interest in mathematical methods!
TEXT:
There is no required text. Copies of
detailed lecture notes which will be made available for download
from this web page; students are expected to make use of the
library to supplement these notes. For those who would like to
buy a text, the following is highly recommended: Linear and
Nonlinear Optimization by Griva, Nash and
Sofer, SIAM Press, Philadelphia, Second edition (2009).
NOTES:
Module 1
Module 2
Module 3
Module 4
Module 5
Module 6
REFERENCES:
Other good references - several of these are on
reserve in the library (in addition to the text):
- Nonlinear Programming: Theory and
Algorithms; by Bazaraa, Sherali and Shetty
- Nonlinear Programming: Analysis and
Methods- Avriel
- Practical Optimization - Gill,
Murray and Wright
- Foundations of Optimization -
Beightler, Phillips and Wilde
- Introduction to Linear and Nonlinear
Programming - Luenberger
- Numerical Optimization Techniques -
Evtushenko
- Numerical Optimization - Nocedal
and Wright
- Nonlinear Programming - Bertsekas
COURSE OUTLINE:
This course is aimed at upper level graduate
students in Engineering, Operations Research, Management Science,
Applied Mathematics, Computer Science and Economics. The objective
is to expose the student to different types of nonlinear
programming problems, and to the various algorithms used to solve
these. While requisite theory will
be covered, the emphasis of the course is neither on formal
theorems and proofs, nor on specific applications, but rather, on
structure and methods. Topics to be covered include (tentatively)
solution of simultaneous nonlinear equations; unconstrained
optimization of single and multiple variable functions by both
derivative and derivative-free methods, including search methods,
conjugate gradient and variable metric algorithms; necessary
conditions for optimality; convexity and convex programming;
constrained optimization including penalty and barrier function
methods, Lagrangian algorithms, and primal methods; Lagrangian
duality; quadratic, fractional, separable and geometric
programming; computer packages to solve NLP problems. Other topics
might be added or some of the above topics might be left out,
depending on time constraints and class interests.
GRADING:
Primarily on the basis of two examinations.
Homework assignments and a paper/project might account for a small
portion of the grade.
HOMEWORK ASSIGNMENTS:
Homework 1 (Solutions)
Homework 2 (Solutions)
Homework 3
Homework 4
Homework 5
Homework 6
Geometric Programming
Software:
Right click on the link to download the file
GPINSTAL.zip and then save it to you
own drive/device. Extract the program (gpglp.exe), support
program (dosxmsf.exe), parameter file (parms.pos), documentation
(GPGLP-manual.*) and three sample data files along with the
output from the optimal solutions. Read the documentation
to learn how to use the program.
Here is a zip file with GGPLAB (for
MATLAB).