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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
39б |
| 1 |
411.25Кб |
| 1.1 Material.pdf |
893.54Кб |
| 1.1 ortools_ex.py |
1.19Кб |
| 1. Congratulation.mp4 |
1.40Мб |
| 1. Congratulation.srt |
775б |
| 1. CP Ortools.mp4 |
50.76Мб |
| 1. CP Ortools.srt |
4.57Кб |
| 1. Installing Python.mp4 |
13.90Мб |
| 1. Installing Python.srt |
3.05Кб |
| 1. Introduction.mp4 |
13.48Мб |
| 1. Introduction.mp4 |
1.01Мб |
| 1. Introduction.srt |
3.52Кб |
| 1. Introduction.srt |
816б |
| 1. Lists, Tuples, and Dictionary.mp4 |
49.19Мб |
| 1. Lists, Tuples, and Dictionary.srt |
9.28Кб |
| 1. LP Introduction.mp4 |
6.26Мб |
| 1. LP Introduction.srt |
3.37Кб |
| 1. MILP Introduction.mp4 |
5.73Мб |
| 1. MILP Introduction.srt |
2.23Кб |
| 1. MINLP Introduction.mp4 |
2.56Мб |
| 1. MINLP Introduction.srt |
1.20Кб |
| 1. NLP Introduction.mp4 |
2.78Мб |
| 1. NLP Introduction.srt |
1.32Кб |
| 1. Pyomo Using other solvers (CBC).mp4 |
36.31Мб |
| 1. Pyomo Using other solvers (CBC).srt |
3.68Кб |
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247.97Кб |
| 11 |
401.54Кб |
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318.20Кб |
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283.23Кб |
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123.95Кб |
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498.20Кб |
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426.57Кб |
| 18 |
193.32Кб |
| 19 |
263.83Кб |
| 2 |
94.64Кб |
| 2.1 ex_area.py |
562б |
| 2.1 inputs.xlsx |
9.40Кб |
| 2.1 Ipopt-3.11.1-win64-intel13.1.zip |
9.89Мб |
| 2.1 Material.pdf |
893.54Кб |
| 2.1 pyomo_ex.py |
721б |
| 2.1 pyomo_ex.py |
672б |
| 2.2 pyomo_array_sum.py |
947б |
| 2.2 pyomo_ex.py |
698б |
| 2. Framework and Solvers.mp4 |
6.00Мб |
| 2. Framework and Solvers.srt |
2.51Кб |
| 2. Garden problem.mp4 |
37.99Мб |
| 2. Garden problem.srt |
4.37Кб |
| 2. If, For, While.mp4 |
54.71Мб |
| 2. If, For, While.srt |
10.77Кб |
| 2. MILP Pyomo.mp4 |
22.60Мб |
| 2. MILP Pyomo.srt |
3.74Кб |
| 2. MINLP Pyomo (Couenne).mp4 |
23.84Мб |
| 2. MINLP Pyomo (Couenne).srt |
3.21Кб |
| 2. NLP Pyomo (IPOPT).mp4 |
25.79Мб |
| 2. NLP Pyomo (IPOPT).srt |
3.00Кб |
| 2. Packages.mp4 |
3.47Мб |
| 2. Packages.srt |
1.45Кб |
| 2. Pyomo Summations.mp4 |
198.18Мб |
| 2. Pyomo Summations.srt |
26.78Кб |
| 2. What is optimization.mp4 |
23.25Мб |
| 2. What is optimization.srt |
5.96Кб |
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358.72Кб |
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507.42Кб |
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213.40Кб |
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15.53Кб |
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164.94Кб |
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454.91Кб |
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30.19Кб |
| 27 |
258.56Кб |
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412.37Кб |
| 29 |
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| 3 |
423.77Кб |
| 3.1 code.py |
106б |
| 3.1 ortools_ex.py |
405б |
| 3.1 ortools_ex.py |
406б |
| 3.1 pyomo_mindtpy_ex.py |
718б |
| 3.1 rotas_input.xlsx |
9.50Кб |
| 3.1 scip_ex.py |
366б |
| 3.2 ex_rota.py |
1.49Кб |
| 3.2 myFile.py |
99б |
| 3. Functions.mp4 |
24.98Мб |
| 3. Functions.srt |
4.82Кб |
| 3. IDE Spyder.mp4 |
13.80Мб |
| 3. IDE Spyder.srt |
2.56Кб |
| 3. LP Ortools.mp4 |
36.24Мб |
| 3. LP Ortools.srt |
7.65Кб |
| 3. MILP Ortools.mp4 |
7.27Мб |
| 3. MILP Ortools.srt |
1.69Кб |
| 3. MINLP Pyomo (decomposition using mindtpy).mp4 |
19.14Мб |
| 3. MINLP Pyomo (decomposition using mindtpy).srt |
2.81Кб |
| 3. NLP SCIP.mp4 |
11.63Мб |
| 3. NLP SCIP.srt |
1.91Кб |
| 3. Pyomo Pprint.mp4 |
19.54Мб |
| 3. Pyomo Pprint.srt |
2.35Кб |
| 3. Route problem.mp4 |
148.38Мб |
| 3. Route problem.srt |
15.00Кб |
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101.74Кб |
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| 4 |
130.92Кб |
| 4.1 code.py |
184б |
| 4.1 ex_receita.py |
582б |
| 4.1 Pyomo - Optimization Modeling in Python (2017, Springer).pdf |
1.76Мб |
| 4.1 scip_ex.py |
340б |
| 4.1 scip_ex.py |
335б |
| 4.1 scip_ex.py |
385б |
| 4.2 pyomo_manual3.pdf |
1.57Мб |
| 4.3 pyomo_manual.pdf |
253.96Кб |
| 4.4 pyomo_manual2.pdf |
10.21Мб |
| 4. Jupyter NotebookLab.mp4 |
9.27Мб |
| 4. Jupyter NotebookLab.srt |
2.88Кб |
| 4. LP SCIP.mp4 |
44.38Мб |
| 4. LP SCIP.srt |
6.53Кб |
| 4. MILP SCIP.mp4 |
8.79Мб |
| 4. MILP SCIP.srt |
1.92Кб |
| 4. MINLP SCIP.mp4 |
7.75Мб |
| 4. MINLP SCIP.srt |
1.26Кб |
| 4. NLP Exercise, solve it by yourself.mp4 |
94.87Мб |
| 4. Numpy.mp4 |
34.65Мб |
| 4. Numpy.srt |
7.41Кб |
| 4. Pyomo Manual.html |
59б |
| 4. Revenue problem.mp4 |
51.51Мб |
| 4. Revenue problem.srt |
3.52Кб |
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255.81Кб |
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300.41Кб |
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237.33Кб |
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245.03Кб |
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1.38Кб |
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279.99Кб |
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26.63Кб |
| 47 |
221.57Кб |
| 48 |
452.73Кб |
| 49 |
247.42Кб |
| 5 |
267.33Кб |
| 5.1 alg_gen.py |
878б |
| 5.1 code.py |
339б |
| 5.1 ex_opflinear.py |
1.90Кб |
| 5.1 exercise_cos.py |
511б |
| 5. Exercises.html |
169б |
| 5. LP Gurobi, CPLEX, and GLPK.mp4 |
77.24Мб |
| 5. LP Gurobi, CPLEX, and GLPK.srt |
10.79Кб |
| 5. MILP Exercise, solve it by yourself.mp4 |
143.91Мб |
| 5. MINLP Genetic Algorithm.mp4 |
37.01Мб |
| 5. MINLP Genetic Algorithm.srt |
6.51Кб |
| 5. NLP Exercise Solution.mp4 |
31.50Мб |
| 5. NLP Exercise Solution.srt |
4.77Кб |
| 5. Optimal power flow problem.mp4 |
127.59Мб |
| 5. Optimal power flow problem.srt |
10.13Кб |
| 5. Pandas.mp4 |
36.58Мб |
| 5. Pandas.srt |
9.87Кб |
| 50 |
439.14Кб |
| 51 |
98.58Кб |
| 52 |
505.42Кб |
| 53 |
130.46Кб |
| 6 |
150.30Кб |
| 6.1 data.xlsx |
10.61Кб |
| 6.1 exercise.py |
964б |
| 6.1 psopy_ex.py |
437б |
| 6.1 pyomo_ex.py |
653б |
| 6.2 pandas_excel.py |
334б |
| 6.3 output.xlsx |
4.89Кб |
| 6. LP Pyomo.mp4 |
50.11Мб |
| 6. LP Pyomo.srt |
8.28Кб |
| 6. MILP Exercise solution.mp4 |
64.84Мб |
| 6. MILP Exercise solution.srt |
8.26Кб |
| 6. MINLP Particle Swarm (PSO).mp4 |
23.47Мб |
| 6. MINLP Particle Swarm (PSO).srt |
3.55Кб |
| 6. Pandas reading Excel.mp4 |
45.72Мб |
| 6. Pandas reading Excel.srt |
7.98Кб |
| 7 |
159.10Кб |
| 7.1 code.py |
136б |
| 7.1 pulp_ex.py |
318б |
| 7. Graphs.mp4 |
19.99Мб |
| 7. Graphs.srt |
4.03Кб |
| 7. LP PuLP.mp4 |
23.56Мб |
| 7. LP PuLP.srt |
4.85Кб |
| 8 |
301.67Кб |
| 8. Exercises.html |
169б |
| 8. Which solver and frameworks should we choose.mp4 |
7.71Мб |
| 8. Which solver and frameworks should we choose.srt |
2.23Кб |
| 9 |
505.36Кб |
| 9.1 exercise.py |
742б |
| 9. LP Exercise, solve it by yourself.mp4 |
70.35Мб |
| TutsNode.com.txt |
63б |