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Hands-On Mathematical Optimization with AMPL in Python

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  • mosek (3 notebooks)

mosek (3 notebooks)#

Hydrothermal Scheduling Problem with Conic Programming#

Notebooks > Hydrothermal Scheduling Problem with Conic Programming
hydrothermal.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In Gradient Open In SageMaker Studio Lab
Description: Hydrothermal Scheduling Problem using Second-Order Cones
Tags: amplpy, conic, second-order-cone, quadratic-cone, nonlinear-programming, scheduling, engineering, power-generation, geothermal-energy, hydropower
Author: Gleb Belov (8 notebooks) <gleb@ampl.com>

Logistic Regression with amplpy#

Notebooks > Logistic Regression with amplpy
logistic_regression.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In Gradient Open In SageMaker Studio Lab
Description: Logistic regression with amplpy using exponential cones
Tags: highlights, amplpy, regression, sigmoid, softplus, log-sum-exp, classifier, regularization, machine-learning, conic, exponential-cone, second-order-cone, quadratic-cone, formulation-comparison
Author: Gleb Belov (8 notebooks) <gleb@ampl.com>, Filipe Brandão (16 notebooks) <fdabrandao@gmail.com>

Robust Linear Programming with Ellipsoidal Uncertainty#

Notebooks > Robust Linear Programming with Ellipsoidal Uncertainty
tip6_robust_linear_programming.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In Gradient Open In SageMaker Studio Lab
Description: AMPL Modeling Tips #6: Robust Linear Programming
Tags: highlights, modeling-tips, conic
Author: Gleb Belov (8 notebooks) <gleb@ampl.com>

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  • Hydrothermal Scheduling Problem with Conic Programming
  • Logistic Regression with amplpy
  • Robust Linear Programming with Ellipsoidal Uncertainty

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