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Titlebook: An Economic Interpretation of Linear Programming; Quirino Paris Book 2016 The Editor(s) (if applicable) and The Author(s) 2016 Linear prog

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https://doi.org/10.1007/978-3-531-90765-9ications. In this chapter we will examine more closely the geometric and algebraic structure of a linear programming model. For ease of exposition and graphing, we will discuss a very simplified linear programming problem with only two outputs and two inputs because we want to graph it in two-dimens
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Die Naturalisierung der Ungleichheitingly, the first method still seems to be the best procedure. The simplex method is “best” in the sense that it is a very efficient procedure for solving a large class of empirical LP problems and, furthermore, every single step can be given an economic interpretation.
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https://doi.org/10.1007/978-3-531-90765-9orking on the primal problem. Analogously, a procedure that solves a dual linear programming problem may be called a dual simplex algorithm. This algorithm was discovered by C. E. Lemke in 1954, seven years after the primal simplex procedure. It is fair to say that without the dual simplex algorithm
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https://doi.org/10.1007/978-3-531-90765-9mpetitive price-taking firm. It is important, however, to realize that duality relations are general features of reality (as interpreted by mathematics) and that the principal method for studying them is the .
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Naturwissenschaft und Erfahrung,rst class contains those problems whose constraints are all inequalities and admit an easy primal feasible basis (the identity matrix .). An equivalent way to regard these problems is to say that the origin of the space (the zero point in the Cartesian coordinate system of the output space) belongs
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Indeterminismus und Quantentheorie,use the dual of a variable is a constraint and vice versa (see chapter 2). Armed with this dual notion (variable ←→ constraint), we can infer the requirements of the artificial constraint algorithm from the requirements of the artificial variable algorithm by simply exchanging every dual notion, as
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https://doi.org/10.1007/978-3-642-51845-4eloped the knowledge of how to interpret economically every component of the optimal solutions. It is time, therefore, to reconsider Stigler’s diet problem, as promised in section 1.6, and to compute its true optimal solution.
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https://doi.org/10.1007/978-3-642-51845-4 solution consisted of collecting and transporting the waste from urban areas to landfills where it was simply buried. Increasing environmental consciousness, coupled with a limited supply of suitable landfills, has raised the price of waste disposal to a level that makes it convenient to look at so
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https://doi.org/10.1007/978-3-663-16090-8uss a LP model applied to range cattle ranching. The main problem of this type of farm operation is the decision about the stock of animals versus the available quantities of feed during the year. Cattle ranching is a dynamic enterprise requiring a sequence of decisions overlapping several productio
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