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    3. Mathematical Optimization in Python :Using PuLP, Python-MIP

    Mathematical Optimization in Python :Using PuLP, Python-MIP

    Posted By: lucky_aut
    Date: 23 Jul 2024 06:05:31
    Mathematical Optimization in Python :Using PuLP, Python-MIP

    Mathematical Optimization in Python :Using PuLP, Python-MIP
    Published 7/2024
    Duration: 3h18m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.45 GB
    Genre: eLearning | Language: English

    A Practical Approach to Solving Optimization Problems: Learn PuLP and Python-MIP Syntax and Features


    What you'll learn
    Basic Usage of Python Libraries such as PuLP and Python-MIP
    Differences and Features of PuLP and Python-MIP
    Fundamentals of Mathematical Optimization including Linear Programming and its Applications
    Classic Problems and Solutions in Mathematical Optimization: Production Planning, Knapsack Problem and Traveling Salesman Problem (TSP)

    Requirements
    How to use list and dictionary types in Python (not necessarily required)

    Description
    Advanced optimization techniques are essential for finding optimal solutions to the increasingly complex operational and long-term planning tasks companies face today. With information changing rapidly, decision-making has become a challenging task. Therefore, professionals in this field are among the most valued in the market.
    In this course, you will learn the necessary skills to solve problems by applying Mathematical Optimization using Linear Programming (LP). We will focus on two powerful Python libraries: PuLP and Python-MIP.
    What You'll Learn:
    Introduction to Mathematical Optimization
    Using PuLP and Python-MIP for optimization problems
    Differences and features of PuLP and Python-MIP
    Practical applications through various problems:
    The Knapsack Problem
    The Traveling Salesman Problem (TSP)
    Production Planning Optimization
    The following solvers and frameworks will be explored:
    Solvers:
    CBC (default solver for both PuLP and Python-MIP)
    Frameworks:
    PuLP and Python-MIP
    The classes use examples created step by step, so we will build the algorithms together. This hands-on approach ensures you can follow along and understand the process of creating and solving optimization models.
    ems. We will also provide an introduction to mathematical modeling, so you can start solving your problems immediately.
    I hope this course can help you in your career.
    Enroll now and start your journey to mastering optimization with Python!
    Who this course is for:
    Individuals eager to gain expertise in leveraging Mathematical Optimization, including Linear Programming, for practical business applications
    Those striving to attain proficiency in the fundamental usage of Python libraries like PuLP and python-mip

    More Info
    UPWARDS