Author:
Almosa Nadia Ali Abbas,Al-Jilawi Ahmed Sabah
Abstract
In this paper, we will define optimization, linear programming, and the duality of linear programming and demonstrate them in practice through several examples in which the Python language was used to display the final outputs using codes for various libraries. We will also illustrate the method of approximation to Tyler by defining the strategy and demonstrating it in practice through an example.
Publisher
Universidad Tecnica de Manabi
Subject
Education,General Nursing
Reference19 articles.
1. Luenberger, D. G., & Ye, Y. (1984). Linear and nonlinear programming (Vol. 2). Reading, MA: Addison-Wesley.
2. Hlawitschka, W. (1994). The empirical nature of Taylor-series approximations to expected utility. The American Economic Review, 84(3), 713-719.
3. Sanner, M. F. (1999). Python: a programming language for software integration and development. J Mol Graph Model, 17(1), 57-61.
4. Herrera, F., & Herrera-Viedma, E. (2000). Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets and systems, 115(1), 67-82.
5. Ben-Tal, A., & Nemirovski, A. (2001). Lectures on modern convex optimization: analysis, algorithms, and engineering applications. Society for industrial and applied mathematics.