Complete Python all you need for LP MILP NLP MINLP Free Video Course

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

Python

What you will learn:

  • Solve optimization problems using linear programming, mixed-integer linear programming, nonlinear programming, mixed-integer nonlinear programming,
  • LP, MILP, NLP, MINLP, SCOP, NonCovex Problems
  • 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 

Requirements of Python:

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

Description of Python:

Operational arranging and long haul anticipating organizations are more unpredictable as of late. Data change quick, and the dynamic is a hard undertaking. Consequently, enhancement calculations are utilized to discover ideal answers for these issues. Experts in this field are the most esteemed ones.

In this course you will realize what is important to tackle issues applying:

  • Straight Programming (LP)
  • Blended Integer Linear Programming (MILP)
  • NonLinear Programming (NLP)
  • Blended Integer Linear Programming (MINLP)
  • Hereditary Algorithm (GA)
  • Molecule Swarm (PSO)
  • Requirement Programming (CP)
  • Second-Order Cone Programming (SCOP)
  • NonConvex Quadratic Programmin (QP)
  • The accompanying solvers and structures will be investigated:
  • Solvers: CPLEX – Gurobi – GLPK – CBC – IPOPT – Couenne – SCIP
  • Systems: Pyomo – Or-Tools – PuLP
  • Same Packages and apparatuses: Geneticalgorithm – Pyswarm – Numpy – Pandas – MatplotLib – Spyder – Jupyter Notebook
  • Additionally, you will figuring out how to apply some linearization methods when utilizing double factors.
  • Notwithstanding the classes and activities, the accompanying issues will be tackled bit by bit:
  • Improvement on the most proficient method to introduce a fence in a nursery

Course improvement issue

  • Boost the income in a rental vehicle store
  • Ideal Power Flow: Electrical Systems
  • The classes use models that are made bit by bit, so we will make the calculations together.

Other than this course is more worried about numerical methodologies, you will likewise figure out how to take care of issues utilizing man-made consciousness (AI), hereditary calculation, and molecule swarm.

Try not to stress on the off chance that you don’t know Python or how to code, I will train you all you require to begin with streamlining, from the establishment of Python and its fundamentals, to complex improvement issues.

I trust this course can help you in your transporter. However, you will get a certificate from Udemy.

See you in the classes!

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

Course content of Python:

Introduction of Python:

  • What is optimization

Installing Python:

  • Installing Python
  • Packages
  • IDE Spyder
  • Jupyter Notebook\Lab
  • Exercises3 questions

Starting with Python:

  • Lists, Tuples, and Dictionary
  • If, For, While
  • Functions
  • Numpy
  • Pandas
  • Pandas: reading Excel
  • Graphs
  • Exercises4 questions
  • PDFs to learn more about Python

Linear Programming (LP):

  • LP: Introduction
  • Framework and Solvers
  • LP: Ortools
  • LP: SCIP
  • LP: Gurobi, CPLEX, and GLPK (installation)
  • LP: Pyomo (using Gurobi, CPLEX, and GLPK)
  • LP: PuLP05:18
  • Which solver and frameworks should we choose?
  • LP: Exercise, solve it by yourself
  • LP: Concepts

Working with Pyomo:

  • Pyomo: Using other solvers (CBC)
  • Pyomo: SummationsPreview
  • Pyomo: Pprint
  • Pyomo: Manual

Mixed-Integer Linear Programming (MILP):

  • MILP: Introduction
  • MILP: Pyomo
  • MILP: Ortools
  • MILP: SCIP
  • MILP: Exercise, solve it by yourself
  • MILP: Exercise solution
  • MILP: Concepts

Nonlinear Programming (NLP):

  • NLP: Introduction
  • NLP: Pyomo (IPOPT)
  • NLP: SCIP
  • NLP: Exercise, solve it by yourself
  • NLP: Exercise Solution
  • NLP: Concepts

Mixed-Integer Nonlinear Programming (MINLP):

  • MINLP: Introduction
  • MINLP: Pyomo (Couenne)
  • MINLP: Pyomo (decomposition using mindtpy)
  • MINLP: SCIP
  • MINLP: Genetic Algorithm
  • MINLP: Genetic Algorithm Concepts
  • MINLP: Particle Swarm (PSO)
  • MINLP: PSO Concepts

Constraint Programming (CP):

  • CP: Ortools
  • CP: Concepts

Special Cases:

  • Introduction
  • SCOP: Second-Order Cone Programming
  • NonConvex Quadratic Programming
  • Vehicle Routing Problems (VRP) with Or-Tools, An introduction
  • Linearization: binary*continuos using BigM
  • Linearization: binary*binary

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