For LP:
Please check the reading list. Ensure that you know sensitivity
analysis and both the revised and dual simplex method.
For NLP:
Be prepared to use a log-barrier, and/or quadratic penalty function
approach, to solve a simple NLP. (This involves the solution of a
family of unconstrained optimization problems.)
In addition,
you should be familiar with the concepts of: gradient; Hessian;
convexity; convex (concave) program; quadratic functions; optimality
conditions for unconstrained and constrained programs;
local vs global optimality; SUMT.
For Networks:
Please check the reading list. In addition, see the notes on WWW
that we have been going over in class. These notes include: min
distance; minimum spanning tree; max flow; transportation problem;
transshipment problem, etc...
For Integer Programming:
Please check the reading list and the notes on WWW. These notes
include several techniques on modelling problems with integer variables
(e.g. logical constraints).
In addition, you need to be able to solve a simple problem using
branch-bound and/or Gomory cutting planes.
For Dynamic Programming:
This involves modelling and solving using a recursion equation. In
particular, we have looked at: networks with no cycles; and networks
with no negative cycles. It is enough
to look at the two examples done in class and to understand the simple
recursions discussed in class.