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    Optimization with Python: all you need for LP-MILP-NLP-MINLP

    Posted By: IrGens
    Optimization with Python: all you need for LP-MILP-NLP-MINLP

    Optimization with Python: all you need for LP-MILP-NLP-MINLP
    .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 6h 57m | 1.85 GB
    Instructor: Rafael Silva Pinto

    Learn how to solve optimization problems using CPLEX, Gurobi, A.I., and more (also called operational research)

    What you'll learn

    Solve optimization problems using linear programming, mixed-integer linear programming, nonlinear programming, mixed-integer nonlinear programming,
    LP, MILP, NLP, MINLP
    Main solvers and frameworks, including CPLEX, Gurobi, and Pyomo
    Genetic algorithm, particle swarm, and constraint programming
    From the basic to advanced tools, learn how to install Python and how to use the main packages (Numpy, Pandas, Matplotlib…)
    How to solve problems with arrays and summations

    Requirements

    Some knowledge in programming logic
    Why and where to use optimization
    It is NOT necessary to know Python

    Description

    Operational planning and long term planning for companies are more complex in recent years. Information change fast, and the decision making is a hard task. Therefore, optimization algorithms are used to find optimal solutions for these problems. Professionals in this field are the most valued ones.

    In this course you will learn what is necessary to solve problems applying:

    Linear Programming (LP)
    Mixed-Integer Linear Programming (MILP)
    NonLinear Programming (NLP)
    Mixed-Integer Linear Programming (MINLP)
    Genetic Algorithm (GA)
    Particle Swarm (PSO)
    Constraint Programming (CP)

    The following solvers and frameworks will be explored:

    Solvers: CPLEX – Gurobi – GLPK – CBC – IPOPT – Couenne – SCIP
    Frameworks: Pyomo – Or-Tools – PuLP
    Same Packages and tools: Geneticalgorithm – Pyswarm – Numpy – Pandas – MatplotLib – Spyder – Jupyter Notebook

    In addition to the classes and exercises, the following problems will be solved step by step:

    Optimization on how to install a fence in a garden
    Route optimization problem
    Maximize the revenue in a rental car store
    Optimal Power Flow: Electrical Systems

    The classes use examples that are created step by step, so we will create the algorithms together.

    Besides this course is more concerned with mathematical approaches, you will also learn how to solve problems using artificial intelligence (AI), genetic algorithm, and particle swarm.

    Don't worry if you do not know Python or how to code, I will teach you everything you need to start with optimization, from the installation of Python and its basics, to complex optimization problems.

    Who this course is for:

    Undergrad, graduation, master program, and doctorate students.
    Companies that wish to solve complex problems
    People interested in complex problems and artificial inteligence


    Optimization with Python: all you need for LP-MILP-NLP-MINLP