What is linear programming? [closed]

Question

I read over the wikipedia article, but it seems to be beyond my comprehension. It says it's for optimization, but how is it different than any other method for optimizing things?

An answer that introduces me to linear programming so I can begin diving into some less beginner-accessible material would be most helpful.

Answer 1

The answers so far have given an algebraic definition of linear programming, and an operational definition. But there is also a geometric definition. A polytope is an n-dimensional generalization of a polygon (in two dimensions) or a polyhedron (in three dimensions). A convex polytope is a polytope which is also a convex set. By definition, linear programming is an optimization problem in which you want to maximize or minimize a linear function on a convex polytope.

For example: Suppose that you want to buy some combination of red sand and blue sand. Suppose also:

1. You can't buy a negative amount of either kind.
2. The depot only has 300 pounds of red sand and 400 pounds of blue sand.
3. Also your jeep has a weight limit of 500 pounds.

If you draw a picture in the plane of how much you can buy with these constraints, it's a convex pentagon. Then, whatever you want to optimize (say, the total amount of gold in the sand), you can know that an optimum (not necessarily the only optimum) is at one of the vertices of the polytope. In fact, there is a much stronger result: Even in high dimensions, any such linear programming problem can be solved in polynomial time, in the number of constraints, or putative sides of the polytope. Note that not every constraint corresponds to a side. If the constraint is an equality, it might reduce the dimension of the polytope. Or if the constraint is an inequality, it might be not create a side if it is already implied by all of the other constraints.

There are a lot of practical optimization problems that are linear programming. One of the first examples was the "diet problem": Given a menu of a bunch of kinds of food, what is the cheapest possible balanced diet? It's a linear programming problem because the cost is linear, and because all of the constraints (vitamins, calories, the assumption that you can't buy a negative amount of food, etc.) are linear.

But, linear programming is even more important for a theoretical reason. It is one of the most powerful polynomial-time algorithms for optimization or for any other purpose. As such, it is very important as a substitute for approximately solving other optimization problems, and as a subroutine for exactly solving them.

Yes, two generalizations are convex programming and integer programming. With some qualificiations, convex programming can work just as well as linear programming, provided that the objective (the thing to maximize) is linear. It turns out that convexity, not flat sides, is the main reason that linear programming has a good algorithm.

Integer programming, on the other hand, is usually hard. For instance, suppose in the example problem you have to buy the sand in fixed-size bags rather than in bulk; that is then integer programming. There is a theorem that it can be NP-hard. How hard it is in practice depends on how close it is to linear programming. There are some celebrated examples of integer programming problems in which, miraculously, all of the vertices of the linear program are integer points. Then you can solve the linear program and the solution will happen to be integral. One example of such a problem is the marriage problem, how to marry n men and n women to each other to maximize total happiness. (Or, n cities to n factories, n jobs to n applicants, n computers to n printers, etc.)

Answer 2

Linear programming is a topic of 'mathematical programming', which is also called 'mathematical optimization'. Linear programs differ from general mathematical programs in that for a Linear Program (LP) all constraint functions and the objective function are linear with respect to their variables.

A good place to start would be here if you want the original work by Dantzig, or if you want to get a textbook, I recommend this one. If you want to look up your own resources, start with looking up the Simplex method--it is a very common technique to solve these programs, or the less common but definitely polynomial time Ellipsoid method. Though I haven't read it all, looking it over quickly also suggests this PDF may be a good place to start. Make sure whatever you end up reading covers duality (and perhaps specifically the Farkas' lemma) as it's a central idea in most LP solvers.

The most natural extensions are either Integer programs (similar to LP's, but all variables must take on integer values--that is, no fractional components) or Convex programming (perhaps a more general extension). A good convex optimization text book is available in PDF form here.