18i-lp - Algorithms Non-Lecture I Linear Programming The...

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Unformatted text preview: Algorithms Non-Lecture I: Linear Programming The greatest flood has the soonest ebb; the sorest tempest the most sudden calm; the hottest love the coldest end; and from the deepest desire oftentimes ensues the deadliest hate. — Socrates Th’ extremes of glory and of shame, Like east and west, become the same. — Samuel Butler, Hudibras Part II, Canto I (c. 1670) Extremes meet, and there is no better example than the haughtiness of humility. — Ralph Waldo Emerson, “Greatness”, in Letters and Social Aims (1876) I Linear Programming ? The maximum flow / minimum cut problem is a special case of a very general class of problems called linear programming . Many other optimization problems fall into this class, including minimum spanning trees and shortest paths, as well as several common problems in scheduling, logistics, and economics. Linear programming was used implicitly by Fourier in the early 1800s, but it was first formalized and applied to problems in economics in the 1930s by Leonid Kantorovich. Kantorivich’s work was hidden behind the Iron Curtain (where it was largely ignored) and therefore unknown in the West. Linear programming was rediscovered and applied to shipping problems in the early 1940s by Tjalling Koopmans. The first complete algorithm to solve linear programming problems, called the simplex method , was published by George Dantzig in 1947. Koopmans first proposed the name “linear programming" in a discussion with Dantzig in 1948. Kantorovich and Koopmans shared the 1975 Nobel Prize in Economics “for their contributions to the theory of optimum allocation of resources”. Dantzig did not; his work was apparently too pure. Koopmans wrote to Kantorovich suggesting that they refuse the prize in protest of Dantzig’s exclusion, but Kantorovich saw the prize as a vindication of his use of mathematics in economics, which his Soviet colleagues had written off as “a means for apologists of capitalism”. A linear programming problem asks for a vector x ∈ IR d that maximizes (or equivalently, minimizes) a given linear function, among all vectors x that satisfy a given set of linear inequalities. The general form of a linear programming problem is the following: maximize d X j = 1 c j x j subject to d X j = 1 a ij x j ≤ b i for each i = 1 .. p d X j = 1 a ij x j = b i for each i = p + 1 .. p + q d X j = 1 a ij x j ≥ b i for each i = p + q + 1 .. n Here, the input consists of a matrix A = ( a ij ) ∈ IR n × d , a column vector b ∈ IR n , and a row vector c ∈ IR d . Each coordinate of the vector x is called a variable . Each of the linear inequalities is called a constraint . The function x 7→ c · x is called the objective function . I will always use d to denote the number of variables, also known as the dimension of the problem. The number of constraints is usually denoted n ....
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This note was uploaded on 12/15/2009 for the course 942 cs taught by Professor A during the Spring '09 term at University of Illinois at Urbana–Champaign.

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18i-lp - Algorithms Non-Lecture I Linear Programming The...

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