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.
"This book is suitable for anyone who wants to study LP and its applications, especially in economics. The content of the book is well structured in order to help readers understand the concepts of LP. ... this is must-read book for anyone who wants to thoroughly study LP, its economic interpretation and the theorems behind the concepts." (Dharma Lesmono, Mathematical Reviews, September, 2018)
"This book, divided in 21 chapters, studies in great extent the field of linear programming with an emphasis to its applications and the economic interpretation of the problem statement and results. ... This book is very easy to follow and presents an excellent overview of the field of linear programming bothfor first time readers and for experienced researchers in the field." (Efstratios Rappos, zbMATH 1364.90001, 2017)
"This book, divided in 21 chapters, studies in great extent the field of linear programming with an emphasis to its applications and the economic interpretation of the problem statement and results. ... This book is very easy to follow and presents an excellent overview of the field of linear programming bothfor first time readers and for experienced researchers in the field." (Efstratios Rappos, zbMATH 1364.90001, 2017)