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所在平台: Udemy |
课程主页: https://www.udemy.com/course/optimization-with-python-complete-pyomo-bootcamp-a-z/
课程评论：没有评论
《Python优化：Pyomo入门A-Z》课程总结 本课程是关于在Python环境中掌握数学优化问题开发的综合指南。鉴于数学优化在工程、管理、经济和运筹学等量化学科中的日益普及，以及Python语言的声望，本课程旨在教授学员如何利用Python来解决各种类型的数学优化问题。 **课程内容涵盖：** * **数学优化类型：** * 线性规划 (LP) * 混合整数线性规划 (MILP) * 非线性规划 (NLP) * 混合整数非线性规划 (MINLP) * **核心技能：** * **问题建模：** 学员将从零开始学习如何定义决策变量、目标函数、约束条件以及参数，为优化问题打下坚实的基础。 * **Python实现：** 重点使用 **Pyomo** 包，将数学模型转化为Python代码。 * **模型构建：** * 定义优化模型的集合 (Sets) 和参数 (Parameters)。 * 将目标函数和约束条件表示为Python函数。 * 从外部数据源（如CSV或Excel文件）导入和读取数据。 * **求解器应用：** 学习使用多种求解器，如CPLEX, IPOPT, COUENNE等，来求解优化问题。 * **实践应用：** 课程通过解决来自工程、生产管理、调度、运输、供应链等多个领域的实际优化问题，帮助学员巩固所学知识。 * **教学结构：** 课程为每种主要的数学规划类型设计了三个示例。前两个示例用于讲解和演示该类型问题的处理方法，第三个示例（挑战题）鼓励学员独立思考和实现，即使是挑战题，也会提供详细的讲解和解决方案。 **目标受众：** 本课程面向所有水平的学习者，从初学者到高级用户，都能从中受益。 通过本课程的学习，学员将能够独立地构建、求解和应用各种数学优化模型，显著提升在定量分析和决策科学领域的技能。
Mathematical Optimization is getting more and more popular in most quantitative disciplines, such as engineering, management, economics, and operations research. Furthermore, Python is one of the most famous programming languages that is getting more attention nowadays. Therefore, we decided to create a course for mastering the development of optimization problems in the Python environment. In this course, you will learn how to deal with various types of mathematical optimization problems as below:Linear Programming (LP)Mixed Integer Linear Programming (MILP)Non-Linear ProgrammingMixed Integer Non-Linear ProgrammingSince this course is designed for all levels (from beginner to advanced), we start from the beginning that you need to formulate a problem. Therefore, after finishing this course, you will be able to find and formulate decision variables, objective function, constraints and define your parameters. Moreover, you will learn how to develop the formulated model in the Python environment (using the Pyomo package). Here are some of the important skills that you will learn when using Python in this course:Defining Sets & Parameters of the optimization model Expressing the objective function and constraints as Python functionImport and read data from an external source (CSV or Excel file)Solve the optimization problem using various solvers such as CPLEX, IPOPT, COUENNE &, etc.In this course, we solve simple to complex optimization problems from various disciplines such as engineering, production management, scheduling, transportation, supply chain, and.areas. This course is structured based on 3 examples for each of the main mathematical programming sections. In the first two examples, you will learn how to deal with that type of specific problem. Then you will be asked to challenge yourself by developing the challenge problem into the Python environment. Nevertheless, even the challenge problem will be explained and solved with details.
