Linear Nonlilear Programming

R Programming: Fundamentals to Advanced is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization. data munging, regression, classification, clustering, modern machine learning and more.

Linear programming has attracted the interest of mathematicians since World War II when the first computers were constructed. Early attempts to apply linear programming methods practical problems failed, in part because of the inexactness of the data used to create the models.

Learn R Programming Language for Data Science, Machine Learning, Data Analysis and Data Visualization with Libraries

Optimization for Decision Making: Linear and Quadratic Models is a first-year graduate level text that illustrates how to formulate real world problems using linear and quadratic models; how to use efficient algorithms – both old and new – for solving these models; and how to draw useful conclusions and derive useful planning information from the output of these algorithms. While almost all the best known books on LP are essentially mathematics books with only very simple modeling examples, this book emphasizes the intelligent modeling of real world problems, and the author presents several illustrative examples and includes many exercises from a variety of application areas.

Data science : Learn statistics behind linear regression and build your own working program from scratch in Python.

A. Planning Company Operations: The General Problem At more or less regular intervals, the management of an industrial enter­ prise is confronted with the problem of planning operations for a coming period. Within this category of management problems falls not only the overall planning of the company's aggregate production but problems of a more limited nature such as, for example, figuring the least-cost combina­ tion of raw materials for given output or the optimal transportation schedule. Any such problem of production planning is most rationally solved in two stages: (i) The first stage is to determine the feasible alternatives. For example, what alternative production schedules are at all compatible with the given capacity limitations? What combinations of raw materials satisfy the given quality specifications for the products? etc.

Learn to write programs to solve linear algebraic problems The Second Edition of this popular textbook provides a highly accessible introduction to the numerical solution of linear algebraic problems.

This textbook provides concise coverage of the basics of linear and integer programming which, with megatrends toward optimization, machine learning, big data, etc., are becoming fundamental toolkits for data and information science and technology. The authors’ approach is accessible to students from almost all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification and computer vision.

This Third Edition introduces the latest theory and applications in optimization. It emphasizes constrained optimization, beginning with linear programming and then …