Python与Gurobi实战：AI协同运筹优化与数学建模进阶_CG群

本课程将Gurobi与Cursor IDE深度融合，系统讲解线性、整数、二次规划及多目标优化，涵盖惰性约束、热启动等硬核技术，结合供应链、排班等真实案例，助你快速掌握企业级决策优化开发流程。

原始标题：Applied Optimization with Python and Gurobi

Applied Optimization with Python and Gurobi

本课程是一门面向工业工程、应用数学、数据科学及软件开发领域的学生与专业人士的实战型高级运筹优化与数学建模进阶课程。课程紧跟现代开发趋势，创新性地将行业顶尖的商业求解器 Gurobi (Gurobipy) 与 AI 驱动的 Cursor IDE 深度融合，旨在教授学员如何将复杂的金融和工业场景转化为精准的数学模型，并构建高效的自动化求解流水线。学习本课程需要具备微积分等初级优化概念和基础的 Python 编程能力，它将彻底打破传统理论教学的枯燥感，通过“AI 协同编程”的全新视角，帮助学员在极短时间内掌握企业级决策优化项目的核心开发流程。

在技术深度与应用场景上，课程实现了从经典规划到前沿机器学习交叉领域的全方位覆盖。核心教学板块不仅涵盖了线性规划（对偶、影子价格）、整数与二进制规划（BigM、MIPGap）以及网络流问题，更深入到二次规划（QP/QCP）等非线性模型与多目标优化（帕累托最优、投资组合）的高级应用；同时，课程直击工业痛点，重点倾囊相授如**子巡回消除（TSP 旅行商问题）、惰性约束（Lazy Constraints）、热启动（MIPStart）**以及参数调优（MIPFocus）等解决大规模复杂问题的核心硬核技术，并打通了 Gurobi 与 Pandas、Excel 的数据交互。通过供应链物流、排班调度、精准聚类与特征选择等真实工业项目，配合数独、N皇后等趣味算法挑战，全面赋能学员解决高价值实际业务决策的能力。

Optimization, Modeling, Gurobipy, Python

What you’ll learn
⚡ Master Smart Problem Formulation: Learn to transform complex financial and industrial challenges into precise mathematical models.
⚡ Pro Coding with Python & Gurobi: Build step-by-step optimization pipelines integrated smoothly with Excel and Pandas.
⚡ Harness AI for Rapid Development: Leverage the AI-powered Cursor IDE to accelerate your coding, debug instantly, and visualize solutions.
⚡ Solve Real-World Industry Projects: Master high-value applications like supply chain logistics, scheduling, and machine learning integration.

Requirements
❗ Basic Programming Skills (especially Python) Fundamental Concepts of Introductory Optimization Target Audience: Students of Industrial Engineering, Applied Mathematics, Management, Computer Engineering, and any student taking an optimization course Operations Research (OR) and Industrial Engineering Professionals Data Science Specialists Web and Software Developers

Description
“This course contains the use of artificial intelligence.”
This hands-on course teachesapplied optimization usingPython and theGurobi solver, with a strong focus on practical modeling and AI-assisted development in theCursor IDE.

Core topics include
•Linear Programming – production problems, duality, shadow prices, Simplex method, reduced costs, and slack variables.

•Integer & Binary Programming – BigM method, MIPGap, solution control, and general constraints (absolute value, min/max, indicator).

•Assignment & Network Problems – assignment problem logic, unimodularity, shortest path, and minimum spanning tree.

•NonLinear Programming – quadratic programming (QP), quadratically constrained programming (QCP), piecewise linear functions, and a warehouse location project.

•Multiobjective optimization – Pareto optimality, weighted sum, lexicographic methods, and portfolio case study.

•Advanced techniques – Irreducible Inconsistent Subsystem (IIS), multiscenario analysis, TSP with subtour elimination, lazy constraints, solution pool, warm starts (MIPStart), and tuning MIPFocus.

•Data integration – connecting Gurobi to Excel and Pandas, matrix-based modeling with addMVar, and performance tuning (CPU threads, TimeLimit).

•Machine learning connections – exact clustering and feature selection using MILP.

•Fun challenges – Sudoku, Magic Square, NQueens, Jealous Husbands, and an AI-driven coding challenge.

Target audience: Students of industrial engineering, applied math, computer science, and management; operations research professionals; data scientists; and developers.

Prerequisites: Basic Python and introductory optimization knowledge.

Goals: Master problem formulation, build production-ready pipelines, use AI for rapid development, and solve real-world logistics, scheduling, and ML integration projects.

Who this course is for
⭐ •⁠ ⁠Students of Industrial Engineering, Applied Mathematics, Management, Computer Engineering, and any student taking an optimization course •⁠ ⁠Operations Research (OR) and Industrial Engineering Professionals •⁠ ⁠Data Science Specialists •⁠ ⁠Web and Software Developers

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