Branch Mathematics and Statistics Faculty and Staff Publications
Document Type
Article
Publication Date
2023
Abstract
Operations research science is defined as the science that is concerned with applying scientific methods to complex problems in managing and directing large systems of people, including resources and tools in various fields, private and governmental work, peace and war, politics, administration, economics, planning and implementation in various domains. It uses scientific methods that take the language of mathematics as a basis for it and uses computer, without which it would not have been possible to achieve numerical solutions to the raised problems, those that need correct solutions, when the solutions abound and the options are multiple, so we need a decision based on correct scientific foundations and takes into account all the circumstances and changes that you can encounter the decision-maker during the course of work, and nothing is left to chance or luck, but rather everything that enters into the account and plays its role in decision-making, and we get that when we use the concepts of neutrosophic science to reformulate what the science of operations research presented in terms of methods and methods to solve many practical problems, so we will present in this research a study aimed at shedding light on the most important methods used to solve nonlinear models, which is the Lagrangian multiplier method for nonlinear models constrained by equality and then reformulated using the concepts of neutrosophic science.
Publication Title
Neutrosophic systems with Applications
Language (ISO)
English
Keywords
Operations Research; Nonlinear Programming; Lagrange Multiplier; Neutrosophic Science; Neutrosophic Nonlinear Programming; Lagrange Neutrosophic Multiplier.
Recommended Citation
Jdid, Maissam and Florentin Smarandache. "Lagrange Multipliers and Neutrosophic Nonlinear Programming Problems Constrained by Equality Constraints." Neutrosophic systems with Applications (2023). https://digitalrepository.unm.edu/math_fsp/579