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2016-11-21
An Economic Interpretation of Linear Programming

Authors: Quirino Paris

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This text covers the basic theory and computation for mathematical modeling in linear programming. It provides a strong background on how to set up mathematical proofs and high-level computation methods, and includes substantial background material and direction. Paris presents an intuitive and novel discussion of what it means to solve a system of equations that is a crucial stepping stone for solving any linear program. The discussion of the simplex method for solving linear programs gives an economic interpretation to every step of the simplex algorithm. The text combines in a unique and novel way the microeconomics of production with the structure of linear programming to give students and scholars of economics a clear notion of what it means, formulating a model of economic equilibrium and the computation of opportunity cost in the presence of many outputs and inputs.

Table of contents (21 chapters)

Front Matter

Introduction to Linear Programming

Primal and Dual LP Problems

Setting Up LP Problems

The Transportation and Transshipment Problems

Spaces, Cones, Bases and Extreme Points

Solving Systems of Equations

Primal Simplex Algorithm: The Price-Taking Firm

The Dual Simplex Algorithm

Linear Programming and the Lagrangean Function

The Artificial Variable Algorithm

The Artificial Constraint Algorithm

The Diet Problem Revisited

Parametric Programming: Input Demand Functions

Parametric Programming: Output Supply Functions

Dealing with Multiple Optimal Solutions

Solid Waste Management

The Choice of Techniques in a Farm Production Model

Cattle Ranch Management

The Measurement of Technical and Economic Efficiency

Decentralized Economic Planning

Theorems of Linear Programming

Back Matter

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2016-11-21 06:12:19
有意思,赞一个!!!!
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2016-11-21 07:41:07
的表格发个部分
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2016-11-21 08:08:11
cmwei333 发表于 2016-11-21 04:18
An Economic Interpretation of Linear Programming

Authors: Quirino Paris
这个数不错的
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2016-11-21 08:13:07
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2016-11-21 08:59:23
Thanks !
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