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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mathematical-optimization-with-gams-and-pyomo-python/
课程评论：没有评论
Coursera上的“使用GAMS和Pyomo（Python）进行数学优化”课程是一门入门课程，旨在教授线性规划、非线性规划、混合整数线性规划和混合整数非线性规划四种优化类型。 课程通过科学、工程和管理领域中的不同示例，逐步引导学习者掌握GAMS和Pyomo这两种常用的优化编程环境。GAMS是商业软件，本课程使用演示许可；Pyomo是Python的开源库，课程将在Google Colaboratory中运行。 课程内容涵盖： * **模型构建：** 读取问题陈述，构建优化模型，识别目标函数、决策变量、约束和参数。 * **GAMS编程：** 编写GAMS优化模型，定义集合 (sets)、变量 (variables)、参数 (parameters)、标量 (scalars) 和方程 (equations)，使用不同的求解器，利用NEOS服务器进行优化，以及从文本、gdx和电子表格文件导入导出数据，设置变量范围和界限。 * **Pyomo编程：** 编写Pyomo优化模型，定义模型 (models)、集合 (sets)、变量 (variables)、参数 (parameters)、约束 (constraints) 和目标函数 (objective function)，使用不同的求解器，利用NEOS服务器进行优化，以及从文本、gdx和电子表格文件导入导出数据，设置变量范围和界限。 通过本课程的学习，学员将能够独立进行优化模型的构建和编码，并熟练运用GAMS和Pyomo工具解决实际问题。
This introductory course to optimization in GAMS and Pyomo (Python) contains 4 modules, namely,Linear programmingNonlinear programmingMixed Integer Linear Programming, andMixed-Integer Nonlinear ProgrammingIn each module, we aim to teach you the basics of each type of optimization through 3 different illustrative examples and 1 assingment from different areas of science, engineering, and management. Using these examples, we aim to gently introduce you to coding in two environments commonly used for optimization, GAMS and Pyomo. GAMS is a licensed software, for which we use a demo license in this course. Pyomo is an open-source package in Python, which we use Google Colaboratory to run. As we proceed through the different examples in each module, we also introduce different functionalities in GAMS and Python, including data import and export.At the end of this course, you will be able to,Read a problem statement and build an optimization modelBe able to identify the objective function, decision variables, constraints, and parametersCode an optimization model in GAMSDefine sets, variables, parameters, scalars, equationsUse different solvers in GAMSLeverage the NEOS server for optimizationImport data from text, gdx, and spreadsheet filesExport data to text, gdx, and spreadsheet filesImpose different variable ranges, and boundsCode an optimization model in PyomoDefine models, sets, variables, parameters, constraints, and objective functionUse different solvers in PyomoLeverage the NEOS server for optimizationImport data from text, gdx, and spreadsheet filesExport data to text, gdx, and spreadsheet filesImpose different variable ranges, and bounds
