AMPL - solve multiple models in parallel |
![multiproc.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL - spreadsheet handling with amplxl |
![amplxl.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Bin Packing Problem with GCG |
![bpp.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Capacitated p-Median Problem with GCG |
![cpmp.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Christmas Model created by ChatGPT |
![christmas.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Development Tutorial 1/6 -- Capacitated Facility Location Problem |
![1_floc.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Development Tutorial 2/6 -- Stochastic Capacitated Facility Location Problem |
![2_stoch_floc.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Development Tutorial 3/6 -- Benders Decomposition via AMPL scripting |
![3_benders_stoch_floc.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Development Tutorial 4/6 -- Benders Decomposition via PYTHON scripting |
![4_benders_in_python_stoch_floc.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Development Tutorial 5/6 -- Parallelizing Subproblem Solves in Benders Decomposition |
![5_benders_parallel_stoch_floc.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Development Tutorial 6/6 -- Implementing Benders Decomposition with ampls |
![6_benders_ampls_stoch_floc.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
AMPL Model Colaboratory Template |
![colab.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Aircrew trainee scheduling with seniority constraints |
![tip8_aircrew_trainees_seniority.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Book Example: Economic equilibria |
![economic_eq_lecture.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Book Example: Transshipment problem |
![net1.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Book Example: diet |
![diet.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Book Example: prod |
![prod.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Book Example: steel |
![steel.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Book Example: transp |
![transp.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
CP-style scheduling model with the numberof operator, solved by a MIP solver |
![sched_numberof.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Capacity expansion of power generation |
![capacity_expansion.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Containers scheduling |
![containers_scheduling.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Debugging Model Infeasibility |
![debug_infeas.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Diet lecture |
![diet_case_study.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Diet model with Google Sheets |
![gspread.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
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Dual-Donor Organ Exchange problem |
![Dual-Donor_Organ_Exchange.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Dynamic routing example |
![Dynamic_routing_example.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Efficient Frontier with Google Sheets |
![efficient_frontier.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
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Employee Scheduling Optimization |
![Employee_Scheduling.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Financial Portfolio Optimization with amplpy |
![amplpyfinance_vs_amplpy.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Google Hashcode 2022 |
![practice_problem.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Hospitals-Residents MIP |
![hospitals_residents.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Hydrothermal Scheduling Problem with Conic Programming |
![hydrothermal.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Introduction to Linear and Integer Programming |
![intro_to_linear_prorgramming.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Introduction to Mathematical Optimization |
![intro_to_optimization.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Jupyter Notebook Integration |
![magics.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Largest small polygon |
![largest_small_polygon.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Logistic Regression with amplpy |
![logistic_regression.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Magic sequences |
![magic_sequences.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Multicommodity transportation problem |
![multmip1.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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N-Queens |
![nqueens.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
NFL Team Rating |
![NFL_Team_Rating.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Network Linear Programs |
![network.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Network design with redundancy |
![electric_grid_with_redundancy.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Nonlinear transportation model |
![nltrans_lecture.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Nonlinear transportation problem example |
![nltrans.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Oil refinery production optimization |
![oil_refining.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Optimization Methods in Finance: Chapter 3 |
![finance_opt_example_3_1.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Optimization of an TV advertising campaign based on TRP, GRP indicators |
![TV_Advertisement_campaign_GRP_TRP.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Optimization of an advertising campaign for launching a new product on the market |
![Advertising_campaign_colab.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Optimize your Christmas Tree to Global Optimality |
![christmas_tree.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
P-Median problem |
![p_median.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Pattern Enumeration |
![pattern_enumeration.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Pattern Generation |
![pattern_generation.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Plot feasible region |
![plot_feasible_region.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Pricing and target-market |
![pricing_and_target_market.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Production Model |
![production_model.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Production model |
![production_model.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Quick Start using Pandas dataframes |
![pandasdiet.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Quick Start using lists and dictionaries |
![nativediet.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Robust Linear Programming with Ellipsoidal Uncertainty |
![tip6_robust_linear_programming.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Roll Cutting - Revision 1 & 2 |
![pattern_tradeoff.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Scheduling Multipurpose Batch Processes using State-Task Networks in Python |
![batch_processessing.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Simple sudoku solver using logical constraints (with GUI) |
![sudoku.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Solution check: discontinuous objective function |
![sol-check.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Solving a nonogram puzzle |
![nonogram.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
![Open In SageMaker Studio Lab](https://camo.githubusercontent.com/62c255fa4d4e987c38098a8b83903840f3ca78819803a335449c2ca35292790a/68747470733a2f2f73747564696f6c61622e736167656d616b65722e6177732f73747564696f6c61622e737667) |
Solving simple stochastic optimization problems with AMPL |
![newsvendor.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Steel industry problem |
![steel_lecture.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Sudoku Generator |
![sudoku_gen.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
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![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Supply chain network |
![supply_chain_simple_routes.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Transportation problem |
![transp_lecture.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
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![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Travelling Salesman Problem with subtour elimination |
![tsp_simple_cuts_generic.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
![Open In Colab](https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667) |
![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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Unit Commitment for Electrical Power Generation |
![unit_commitment.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
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![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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VPSolver: Cutting & Packing Problems |
![vpsolver.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
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![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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amplpy setup & Quick Start |
![quickstart.ipynb](https://camo.githubusercontent.com/3a3ecf780ef79ecfe65b3835a54f8b608cf53621d68bf85fcfea8eabd286bcc3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f6c6f676f3d676974687562) |
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![Kaggle](https://camo.githubusercontent.com/c7135949c5c6882489e68f4af05a78a759460a4db256b86df3097e04419b4d9e/68747470733a2f2f6b6167676c652e636f6d2f7374617469632f696d616765732f6f70656e2d696e2d6b6167676c652e737667) |
![Gradient](https://camo.githubusercontent.com/6df71b7d7e0b09a2e97776f416bcd40acf48fc87337f8bcce48e4235537daf1f/68747470733a2f2f6173736574732e706170657273706163652e696f2f696d672f6772616469656e742d62616467652e737667) |
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