1.2.6 Linear Programming
INPUT OUTPUT
Input Description:
A set of linear inequalities, a linear objective
function.
Problem:
Find the assignment to the variables maximizing the objective
function while satisfying all inequalities.
Excerpt from
The Algorithm Design Manual:
The standard algorithm for linear programming is called the simplex method. Each constraint in a
linear programming problem acts like a knife that carves away a region from the space of possible solutions.
We seek the point within the remaining region that maximizes (or minimizes) $f(X)$. By appropriately rotating
the solution space, the optimal point can always be made to be the highest point in the region. Since the
region (simplex) formed by the intersection of a set of linear constraints is convex, we can find the highest
point by starting from any vertex of the region and walking to a higher neighboring vertex. When there is no
higher neighbor, we are at the highest point. While the basic simplex algorithm is not too difficult to program, there is a considerable art to producing
an efficient implementation capable of solving large linear programs. For example, large programs tend to be
sparse (meaning that most inequalities use few variables), so sophisticated data structures must be used.
There are issues of numerical stability and robustness, as well as which neighbor we should walk to next (so
called pivoting rules). Finally, there exist sophisticated interior-point methods, which
cut through the interior of the simplex instead of walking along the outside, that beat simplex in many
applications.
Recommended Books
Related Links
Optimization Frequently Asked Questions
COIN|OR: COmputational INfrastructure for Operations Research
Branch Cut and Price Resource Web
Related Problems
This page last modified on 2008-07-10
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