Logo

Decision Making and Productivity Measurement

Small book cover: Decision Making and Productivity Measurement

Decision Making and Productivity Measurement
by

Publisher: arXiv
Number of pages: 214

Description:
I wrote this book as a self-teaching tool to assist every teacher, student, mathematician or non-mathematician for educating herself or others, and to support their understanding of the elementary concepts on assessing the performance of a set of homogenous firms, as well as how to correctly adapt mathematics to these concepts step by step, in order to underpin this area and rebuild the foundation and columns of efficiency measurement for further research.

Home page url

Download or read it online for free here:
Download link
(4.6MB, PDF)

Similar books

Book cover: Applied Mathematical ProgrammingApplied Mathematical Programming
by - Addison-Wesley
This book shows you how to model a wide array of problems. Covered are topics such as linear programming, duality theory, sensitivity analysis, network/dynamic programming, integer programming, non-linear programming, and my favorite, etc.
(11574 views)
Book cover: Convex Optimization: Algorithms and ComplexityConvex Optimization: Algorithms and Complexity
by - arXiv.org
This text presents the main complexity theorems in convex optimization and their algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural and stochastic optimization.
(2004 views)
Book cover: Discrete OptimizationDiscrete Optimization
by - Utrecht University
From the table of contents: Preliminaries (Optimization Problems); Minimum Spanning Trees; Matroids; Shortest Paths; Maximum Flows; Minimum Cost Flows; Matchings; Integrality of Polyhedra; Complexity Theory; Approximation Algorithms.
(4803 views)
Book cover: Data Assimilation: A Mathematical IntroductionData Assimilation: A Mathematical Introduction
by - arXiv.org
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation. Authors develop a framework in which a Bayesian formulation of the problem provides the bedrock for the derivation and analysis of algorithms.
(2153 views)