# linear programming

linear programming
any of several methods for finding where a given linear function of several nonnegative variables assumes an extreme value and for determining the extreme value, the variable usually being subjected to constraints in the form of linear equalities or inequalities.
[1945-50]

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Mathematical modeling technique useful for guiding quantitative decisions in business, industrial engineering, and to a lesser extent the social and physical sciences.

Solving a linear programming problem can be reduced to finding the optimum value (see optimization) of a linear equation (called an objective function), subject to a set of constraints expressed as inequalities. The number of inequalities and variables depends on the complexity of the problem, whose solution is found by solving the system of inequalities like a system of equations. The extensive use of linear programming during World War II to deal with transportation, scheduling and allocations of resources under constraints like cost and priority gave the subject an impetus that carried it into the postwar era. The number of equations and variables needed to model real-life situations accurately is large, and the solution process can be time-consuming even with computers. See also simplex method.

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mathematical modeling technique useful for guiding quantitative decisions in business planning, industrial engineering, and—to a lesser extent—in the social and physical sciences.

Applications of the method of linear programming were first seriously attempted in the late 1930s by the Soviet mathematician Leonid Kantorovich and by the American economist Wassily Leontief in the areas of manufacturing schedules and of economics, respectively, but their work was ignored for decades. During World War II, linear programming was used extensively to deal with transportation, scheduling, and allocation of resources subject to certain restrictions such as costs and availability. These applications did much to establish the acceptability of this method, which gained further impetus in 1947 with the introduction of the American mathematician George Dantzig's simplex method, which greatly simplified the solution of linear programming problems. However, as increasingly more complex problems involving more variables were attempted, the number of necessary operations expanded exponentially and exceeded the computational capacity of even the most powerful computers. Then, in 1979, the Russian mathematician Leonid Khachian discovered a polynomial-time algorithm—i.e., the number of computational steps grows as a power of the number of variables, rather than exponentially—thereby allowing the solution of hitherto inaccessible problems.

The solution of a linear-programming problem reduces to finding the optimum value (largest or smallest, depending on the problem) of the linear expression (called the objective function):

subject to a set of constraints expressed as inequalities:

The a's, b's, and c's are constants determined by the capacities, needs, costs, profits, and other requirements and restrictions of the problem. The basic assumption in the application of this method is that the various relationships between demand and availability are linear; that is, none of the xi is raised to a power other than 1. In order to obtain the solution to this problem it is necessary to find the solution of the system of linear inequalities (that is, the set of n values of the variables xi that simultaneously satisfies all the inequalities). The objective function is then evaluated by substituting the values of the xi in the equation that defines f .

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Universalium. 2010.

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