Merge branch 'master' of https://github.com/eduardosalaz/colab.ampl.com

Author: eduardosalaz

authors/eduardosalaz/bilevel/bilevel_markets.ipynb

@@ -270,7 +270,9 @@
         "#### Objective Function\n",
         "\n",
         "$$\n",
+        "\\begin{align}\n",
         "\\max_{\\substack{\\text{bid}_i, p_i, \\lambda \\\\ \\mu_i^-, \\mu_i^+, \\nu}} \\quad \\sum_{i \\in \\mathcal{S}} \\left( \\lambda \\cdot p_i - c_i \\cdot p_i - q_i \\cdot p_i^2 \\right) \\tag{MPEC-Obj}\n",
+        "\\end{align}\n",
         "$$\n",
         "\n",
         "#### Constraints\n",

authors/glebbelov/conic/logistic_regression.ipynb

@@ -11,7 +11,7 @@
         "\n",
         "Description: Logistic regression with amplpy using exponential cones\n",
         "\n",
-        "Tags: highlights, amplpy, regression, sigmoid, softplus, log-sum-exp, classifier, regularization, machine learning, conic, exponential cone, second-order cone, quadratic cone, formulation comparison\n",
+        "Tags: highlights, amplpy, logistic regression, regression, sigmoid, softplus, log-sum-exp, classifier, regularization, machine learning, conic, exponential cone, second-order cone, quadratic cone, formulation comparison\n",
         "\n",
         "Notebook author: Gleb Belov <<gleb@ampl.com>>, Filipe Brandão <<fdabrandao@gmail.com>>\n",
         "\n",
@@ -1508,7 +1508,7 @@
         "id": "WVqiAEgp0qvs"
       },
       "source": [
-        "## Evaluate perfornace the optimal $\\theta$ for each lambda on the test set"
+        "## Evaluate performance of the optimal $\\theta$ for each lambda on the test set"
       ]
     },
     {

authors/marcos-dv/geometry/largest_small_polygon.ipynb

@@ -854,7 +854,7 @@
         "\n",
         "There are other models that might be more efficient, these assume *Graham Conjecture*, related to symmetry properties in the diameter graph.\n",
         "\n",
-        "* Multiple non-linear solvers could be used to tackle this problem when the number of sides become big: knitro, ipopt, octeract. For this particular problem, **knitro** (13.2.0) gave the best performance in terms of speed and optimality of solutions."
+        "* Multiple non-linear solvers could be used to tackle this problem when the number of sides become big: knitro, ipopt. For this particular problem, **knitro** (13.2.0) gave the best performance in terms of speed and optimality of solutions."
       ]
     }
   ],

docs/source/authors/fdabrandao_at_gmail.com.rst

@@ -162,7 +162,7 @@ Logistic Regression with amplpy
 | `Notebooks <../notebooks/index.html>`_ > `Logistic Regression with amplpy <../notebooks/logistic-regression-with-amplpy.html>`_
 | |github-logistic-regression-with-amplpy| |colab-logistic-regression-with-amplpy| |deepnote-logistic-regression-with-amplpy| |kaggle-logistic-regression-with-amplpy| |gradient-logistic-regression-with-amplpy| |sagemaker-logistic-regression-with-amplpy|
 | Description: Logistic regression with amplpy using exponential cones
-| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
+| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-logistic-regression`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
 | Author: :ref:`email-gleb_at_ampl.com` <gleb@ampl.com>, :ref:`email-fdabrandao_at_gmail.com` <fdabrandao@gmail.com>
 
 .. |github-logistic-regression-with-amplpy|  image:: https://img.shields.io/badge/github-%23121011.svg?logo=github

docs/source/authors/gleb_at_ampl.com.rst

@@ -110,7 +110,7 @@ Logistic Regression with amplpy
 | `Notebooks <../notebooks/index.html>`_ > `Logistic Regression with amplpy <../notebooks/logistic-regression-with-amplpy.html>`_
 | |github-logistic-regression-with-amplpy| |colab-logistic-regression-with-amplpy| |deepnote-logistic-regression-with-amplpy| |kaggle-logistic-regression-with-amplpy| |gradient-logistic-regression-with-amplpy| |sagemaker-logistic-regression-with-amplpy|
 | Description: Logistic regression with amplpy using exponential cones
-| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
+| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-logistic-regression`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
 | Author: :ref:`email-gleb_at_ampl.com` <gleb@ampl.com>, :ref:`email-fdabrandao_at_gmail.com` <fdabrandao@gmail.com>
 
 .. |github-logistic-regression-with-amplpy|  image:: https://img.shields.io/badge/github-%23121011.svg?logo=github

docs/source/index.rst

@@ -1615,7 +1615,7 @@ Logistic Regression with amplpy
 | `Notebooks <notebooks/index.html>`_ > `Logistic Regression with amplpy <notebooks/logistic-regression-with-amplpy.html>`_
 | |github-logistic-regression-with-amplpy| |colab-logistic-regression-with-amplpy| |deepnote-logistic-regression-with-amplpy| |kaggle-logistic-regression-with-amplpy| |gradient-logistic-regression-with-amplpy| |sagemaker-logistic-regression-with-amplpy|
 | Description: Logistic regression with amplpy using exponential cones
-| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
+| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-logistic-regression`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
 | Author: :ref:`email-gleb_at_ampl.com` <gleb@ampl.com>, :ref:`email-fdabrandao_at_gmail.com` <fdabrandao@gmail.com>
 
 .. |github-logistic-regression-with-amplpy|  image:: https://img.shields.io/badge/github-%23121011.svg?logo=github

docs/source/modules/coin.rst

@@ -366,7 +366,7 @@ Logistic Regression with amplpy
 | `Notebooks <../notebooks/index.html>`_ > `Logistic Regression with amplpy <../notebooks/logistic-regression-with-amplpy.html>`_
 | |github-logistic-regression-with-amplpy| |colab-logistic-regression-with-amplpy| |deepnote-logistic-regression-with-amplpy| |kaggle-logistic-regression-with-amplpy| |gradient-logistic-regression-with-amplpy| |sagemaker-logistic-regression-with-amplpy|
 | Description: Logistic regression with amplpy using exponential cones
-| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
+| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-logistic-regression`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
 | Author: :ref:`email-gleb_at_ampl.com` <gleb@ampl.com>, :ref:`email-fdabrandao_at_gmail.com` <fdabrandao@gmail.com>
 
 .. |github-logistic-regression-with-amplpy|  image:: https://img.shields.io/badge/github-%23121011.svg?logo=github

docs/source/modules/index.rst

@@ -10,7 +10,7 @@ List of modules available:
 
 - Open-source: ``highs``, ``cbc``, ``coin`` (includes: CBC, Couenne, Ipopt, Bonmin), ``open`` (includes all open-source solvers)
 - `NEOS Server <https://www.neos-server.org/>`_: ``gokestrel`` (`kestrel client <https://dev.ampl.com/solvers/kestrel.html>`_)
-- Commercial solvers: ``baron``, ``conopt``, ``copt``, ``cplex``, ``gurobi``, ``knitro``, ``lgo``, ``lindoglobal``, ``loqo``, ``minos``, ``mosek``, ``octeract``, ``snopt``, ``xpress``
+- Commercial solvers: ``baron``, ``conopt``, ``copt``, ``cplex``, ``gurobi``, ``knitro``, ``lgo``, ``lindoglobal``, ``loqo``, ``minos``, ``mosek``, ``snopt``, ``xpress``
 - AMPL Plugins: ``amplgsl`` (`amplgsl docs <https://amplgsl.readthedocs.io/>`_), ``plugins`` (`amplplugins docs <https://amplplugins.readthedocs.io/>`_)
 
 .. note::
@@ -48,6 +48,7 @@ Notebooks grouped by modules
 .. toctree::
     :maxdepth: 2
 
+    baron
     cbc
     coin
     copt

docs/source/modules/mosek.rst

@@ -42,7 +42,7 @@ Logistic Regression with amplpy
 | `Notebooks <../notebooks/index.html>`_ > `Logistic Regression with amplpy <../notebooks/logistic-regression-with-amplpy.html>`_
 | |github-logistic-regression-with-amplpy| |colab-logistic-regression-with-amplpy| |deepnote-logistic-regression-with-amplpy| |kaggle-logistic-regression-with-amplpy| |gradient-logistic-regression-with-amplpy| |sagemaker-logistic-regression-with-amplpy|
 | Description: Logistic regression with amplpy using exponential cones
-| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
+| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-logistic-regression`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
 | Author: :ref:`email-gleb_at_ampl.com` <gleb@ampl.com>, :ref:`email-fdabrandao_at_gmail.com` <fdabrandao@gmail.com>
 
 .. |github-logistic-regression-with-amplpy|  image:: https://img.shields.io/badge/github-%23121011.svg?logo=github

docs/source/tags/amplpy.rst

@@ -1029,7 +1029,7 @@ Logistic Regression with amplpy
 | `Notebooks <../notebooks/index.html>`_ > `Logistic Regression with amplpy <../notebooks/logistic-regression-with-amplpy.html>`_
 | |github-logistic-regression-with-amplpy| |colab-logistic-regression-with-amplpy| |deepnote-logistic-regression-with-amplpy| |kaggle-logistic-regression-with-amplpy| |gradient-logistic-regression-with-amplpy| |sagemaker-logistic-regression-with-amplpy|
 | Description: Logistic regression with amplpy using exponential cones
-| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
+| Tags: :ref:`tag-highlights`, :ref:`tag-amplpy`, :ref:`tag-logistic-regression`, :ref:`tag-regression`, :ref:`tag-sigmoid`, :ref:`tag-softplus`, :ref:`tag-log-sum-exp`, :ref:`tag-classifier`, :ref:`tag-regularization`, :ref:`tag-machine-learning`, :ref:`tag-conic`, :ref:`tag-exponential-cone`, :ref:`tag-second-order-cone`, :ref:`tag-quadratic-cone`, :ref:`tag-formulation-comparison`
 | Author: :ref:`email-gleb_at_ampl.com` <gleb@ampl.com>, :ref:`email-fdabrandao_at_gmail.com` <fdabrandao@gmail.com>
 
 .. |github-logistic-regression-with-amplpy|  image:: https://img.shields.io/badge/github-%23121011.svg?logo=github