| 0 | 0 B | ||
| 1. CP Ortools.mp4 | 50.8 MB | ||
| 1. CP Ortools.srt | 4.6 KB | ||
| 1. Congratulation.mp4 | 1.4 MB | ||
| 1. Congratulation.srt | 819.2 B | ||
| 1. Installing Python.mp4 | 13.9 MB | ||
| 1. Installing Python.srt | 3 KB | ||
| 1. Introduction.mp4 | 13.5 MB | ||
| 1. Introduction.srt | 3.5 KB | ||
| 1. LP Introduction.mp4 | 6.3 MB | ||
| 1. LP Introduction.srt | 3.4 KB | ||
| 1. Lists, Tuples, and Dictionary.mp4 | 49.2 MB | ||
| 1. Lists, Tuples, and Dictionary.srt | 9.3 KB | ||
| 1. MILP Introduction.mp4 | 5.7 MB | ||
| 1. MILP Introduction.srt | 2.2 KB | ||
| 1. MINLP Introduction.mp4 | 2.6 MB | ||
| 1. MINLP Introduction.srt | 1.2 KB | ||
| 1. NLP Introduction.srt | 1.3 KB | ||
| 1. Pyomo Using other solvers (CBC).mp4 | 36.3 MB | ||
| 1. Pyomo Using other solvers (CBC).srt | 3.7 KB | ||
| 1 | 411.2 KB | ||
| 1. NLP Introduction.mp4 | 2.8 MB | ||
| 1.1 Material.pdf | 893.5 KB | ||
| 1.1 ortools_ex.py | 1.2 KB | ||
| 2. Framework and Solvers.mp4 | 6 MB | ||
| 2. Framework and Solvers.srt | 2.5 KB | ||
| 2. Garden problem.mp4 | 38 MB | ||
| 2. Garden problem.srt | 4.4 KB | ||
| 2. If, For, While.mp4 | 54.7 MB | ||
| 2. MILP Pyomo.mp4 | 22.6 MB | ||
| 2. MILP Pyomo.srt | 3.7 KB | ||
| 2. MINLP Pyomo (Couenne).srt | 3.2 KB | ||
| 2. NLP Pyomo (IPOPT).mp4 | 25.8 MB | ||
| 2. NLP Pyomo (IPOPT).srt | 3 KB | ||
| 2. Packages.mp4 | 3.5 MB | ||
| 2. Packages.srt | 1.4 KB | ||
| 2 | 94.6 KB | ||
| 2. If, For, While.srt | 10.8 KB | ||
| 2. MINLP Pyomo (Couenne).mp4 | 23.8 MB | ||
| 2. Pyomo Summations.mp4 | 198.2 MB | ||
| 2. Pyomo Summations.srt | 26.8 KB | ||
| 2. What is optimization.mp4 | 23.2 MB | ||
| 2. What is optimization.srt | 6 KB | ||
| 2.1 Ipopt-3.11.1-win64-intel13.1.zip | 9.9 MB | ||
| 2.1 Material.pdf | 893.5 KB | ||
| 2.1 ex_area.py | 512 B | ||
| 2.1 inputs.xlsx | 9.4 KB | ||
| 2.1 pyomo_ex.py | 716.8 B | ||
| 2.2 pyomo_array_sum.py | 921.6 B | ||
| 2.2 pyomo_ex.py | 716.8 B | ||
| 3. Functions.mp4 | 25 MB | ||
| 3. Functions.srt | 4.8 KB | ||
| 3. IDE Spyder.mp4 | 13.8 MB | ||
| 3. IDE Spyder.srt | 2.6 KB | ||
| 3. LP Ortools.mp4 | 36.2 MB | ||
| 3. LP Ortools.srt | 7.7 KB | ||
| 3. MILP Ortools.mp4 | 7.3 MB | ||
| 3. MILP Ortools.srt | 1.7 KB | ||
| 3. MINLP Pyomo (decomposition using mindtpy).mp4 | 19.1 MB | ||
| 3. MINLP Pyomo (decomposition using mindtpy).srt | 2.8 KB | ||
| 3. NLP SCIP.mp4 | 11.6 MB | ||
| 3. NLP SCIP.srt | 1.9 KB | ||
| 3. Pyomo Pprint.mp4 | 19.5 MB | ||
| 3. Pyomo Pprint.srt | 2.3 KB | ||
| 3. Route problem.mp4 | 148.4 MB | ||
| 3. Route problem.srt | 15 KB | ||
| 3 | 423.8 KB | ||
| 3.1 code.py | 102.4 B | ||
| 3.1 ortools_ex.py | 409.6 B | ||
| 3.1 pyomo_mindtpy_ex.py | 716.8 B | ||
| 3.1 rotas_input.xlsx | 9.5 KB | ||
| 3.1 scip_ex.py | 409.6 B | ||
| 3.2 ex_rota.py | 1.5 KB | ||
| 3.2 myFile.py | 102.4 B | ||
| 4. Jupyter NotebookLab.mp4 | 9.3 MB | ||
| 4. Jupyter NotebookLab.srt | 2.9 KB | ||
| 4. LP SCIP.mp4 | 44.4 MB | ||
| 4. LP SCIP.srt | 6.5 KB | ||
| 4. MILP SCIP.mp4 | 8.8 MB | ||
| 4. MILP SCIP.srt | 1.9 KB | ||
| 4. MINLP SCIP.mp4 | 7.8 MB | ||
| 4. MINLP SCIP.srt | 1.3 KB | ||
| 4. Numpy.mp4 | 34.6 MB | ||
| 4. Revenue problem.mp4 | 51.5 MB | ||
| 4. Revenue problem.srt | 3.5 KB | ||
| 4 | 130.9 KB | ||
| 4. NLP Exercise, solve it by yourself.mp4 | 94.9 MB | ||
| 4. Numpy.srt | 7.4 KB | ||
| 4. Pyomo Manual.html | 102.4 B | ||
| 4.1 Pyomo - Optimization Modeling in Python (2017, Springer).pdf | 1.8 MB | ||
| 4.1 code.py | 204.8 B | ||
| 4.1 ex_receita.py | 614.4 B | ||
| 4.1 scip_ex.py | 409.6 B | ||
| 4.2 pyomo_manual3.pdf | 1.6 MB | ||
| 4.3 pyomo_manual.pdf | 254 KB | ||
| 4.4 pyomo_manual2.pdf | 10.2 MB | ||
| 5. Exercises.html | 204.8 B | ||
| 5. LP Gurobi, CPLEX, and GLPK.mp4 | 77.2 MB | ||
| 5. MILP Exercise, solve it by yourself.mp4 | 143.9 MB | ||
| 5. MINLP Genetic Algorithm.mp4 | 37 MB | ||
| 5. MINLP Genetic Algorithm.srt | 6.5 KB | ||
| 5 | 267.3 KB | ||
| 5. LP Gurobi, CPLEX, and GLPK.srt | 10.8 KB | ||
| 5. NLP Exercise Solution.mp4 | 31.5 MB | ||
| 5. NLP Exercise Solution.srt | 4.8 KB | ||
| 5. Optimal power flow problem.mp4 | 127.6 MB | ||
| 5. Optimal power flow problem.srt | 10.1 KB | ||
| 5. Pandas.mp4 | 36.6 MB | ||
| 5. Pandas.srt | 9.9 KB | ||
| 5.1 alg_gen.py | 921.6 B | ||
| 5.1 code.py | 307.2 B | ||
| 5.1 ex_opflinear.py | 1.9 KB | ||
| 5.1 exercise_cos.py | 512 B | ||
| 6. LP Pyomo.mp4 | 50.1 MB | ||
| 6. LP Pyomo.srt | 8.3 KB | ||
| 6. MILP Exercise solution.mp4 | 64.8 MB | ||
| 6. MILP Exercise solution.srt | 8.3 KB | ||
| 6. MINLP Particle Swarm (PSO).mp4 | 23.5 MB | ||
| 6. Pandas reading Excel.mp4 | 45.7 MB | ||
| 6. Pandas reading Excel.srt | 8 KB | ||
| 6 | 150.3 KB | ||
| 6. MINLP Particle Swarm (PSO).srt | 3.6 KB | ||
| 6.1 data.xlsx | 10.6 KB | ||
| 6.1 exercise.py | 921.6 B | ||
| 6.1 psopy_ex.py | 409.6 B | ||
| 6.1 pyomo_ex.py | 614.4 B | ||
| 6.2 pandas_excel.py | 307.2 B | ||
| 6.3 output.xlsx | 4.9 KB | ||
| 7. Graphs.mp4 | 20 MB | ||
| 7. Graphs.srt | 4 KB | ||
| 7. LP PuLP.mp4 | 23.6 MB | ||
| 7. LP PuLP.srt | 4.9 KB | ||
| 7 | 159.1 KB | ||
| 7.1 code.py | 102.4 B | ||
| 7.1 pulp_ex.py | 307.2 B | ||
| 8 | 301.7 KB | ||
| 8. Exercises.html | 204.8 B | ||
| 8. Which solver and frameworks should we choose.mp4 | 7.7 MB | ||
| 8. Which solver and frameworks should we choose.srt | 2.2 KB | ||
| 9. LP Exercise, solve it by yourself.mp4 | 70.4 MB | ||
| 9 | 505.4 KB | ||
| 9.1 exercise.py | 716.8 B | ||
| TutsNode.com.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614.4 B | ||
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| ▲ 194 total files | |||
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.
I hope this course can help you in your carrier. Yet, you will receive a certification 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 intelligence
Requirements
Some knowledge in programming logic
Why and where to use optimization
It is NOT necessary to know Python
Last Updated 4/2021
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Udemy - Microsoft Copilot for Azure - Cloud Management and Optimization Posted by
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