Instructor: Prof. Katta G. Murty
Office: 2773 IOE Bldg.
Prerequisites: A course in linear or matrix algebra.
Background Required: Elementary matrix algebra(concept of linear
independence, bases, matrix inversion, pivotal methods for solving linear
equations), geometry of Rn including convex sets and affine spaces.
- K. G. Murty, Operations
Research: Deterministic Optimization Models, Prentice Hall, 1995.
- K. G. Murty, Linear
Programming, Wiley, 1983.
- M.S. Bazaraa, J. J. Jarvis,
and H. D. Shirali, Linear Programming and Network Flows,
- R. Saigal, Linear
Programming: A Modern Integrated Analysis, Kluwer, 1995.
- D. Bertsimas and J. N.
Tsitsiklis,Introduction to Linear Optimization, Athena,
- R. Fourer, D. M. Gay, and B.
W. Kernighan, AMPL: A Modeling Language for Mathematical Programming,
Scientific Press, 1993.
- Linear Programming models and
their various applications. Separable piece-wise linear convex function
minimization problems, uses in curve fitting and linear parameter
estimation. Approaches for solving multi-objective linear programming
models, the Goal programming technique.
- What useful planning
information can be derived from an LP model (marginal values and their
- Pivot operations on systems
of linear equations, basic vectors, basic solutions, and bases. Brief
review of the geometry of convex polyhedra.
- Duality and optimality
conditions for LP.
- Revised primal and dual
simplex methods for LP.
- Infeasibility analysis,
marginal analysis, cost coefficient and right hand side constant ranging,
and other sensitivity analyses.
- Algorithm for transportation
- Bounded variable primal
- Brief review of Interior
point methods for LP.
- Weekly Homework Assignments.
- Final Exam
- Two Computational Projects to
be solved using AMPL.
Appoximateweights for determining final grade are: Homeworks(15%),
Midterm(20%), Final Exam(50%), Computer Projects(15%).
Last Update on 04/18/02
By Junghoon Hyun