Udemy MaThematical Optimization in Python Using PuLP Python-MIP

[AD-TEAM]

1.46 GB | 00:17:33 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
1 Introduction (11.23 MB)
2 What is Mathematical Optimization (14.47 MB)
3 PuLP and Python-MIP (25.93 MB)
1 Installing Python (23.49 MB)
2 Installing Visual Studio Code (17.79 MB)
3 Installing VScode Extension (20.25 MB)
4 How to create virtual env (12.27 MB)
1 Basic Syntax of PuLP (23.18 MB)
10 Knapsack Optimization Using lpDot (28 MB)
11 Quick Guide to Dictionary (Reference) (46.01 MB)
12 Knapsack Problem with Larger Datasets (151.96 MB)
13 Overview of the Traveling Salesman Problem (TSP) (40.27 MB)
14 Solving Traveling Salesman Problem ① (65.63 MB)
15 Solving Traveling Salesman Problem ② (104.53 MB)
16 Solving Traveling Salesman Problem ③ (66.98 MB)
2 System of Linear Equations (47.39 MB)
3 Simplifying previous code (24.06 MB)
4 Production Planning Optimization ① (66.59 MB)
5 Production Planning Optimization ② (78.8 MB)
6 Production Planning Optimization ② with Integer Constraints (45.39 MB)
7 Quick Guide to List Comprehensions (Reference) (16.3 MB)
8 Introduction to the Knapsack Problem (7.39 MB)
9 Knapsack Optimization Using lpSum (80.02 MB)
1 PuLP vs Python-MIP (8.53 MB)
2 Syntax Differences Between PuLP and Python-MIP (17.71 MB)
3 System of Linear Equations (17.57 MB)
4 Production Planning Optimization ① (65.65 MB)
5 Production Planning Optimization ② (80.84 MB)
6 Production Planning Optimization ② with Integer Constraints (31.5 MB)
7 Knapsack Optimization (75.41 MB)
8 Knapsack Problem with Larger Datasets (87.79 MB)
9 Traveling Salesman Problem (87.71 MB)

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