Add Ipopt, Bonmin, Couenne

ampl · Aug 29, 2024 · 3e64581 · 3e64581
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diff --git a/docs/source/solvers/_tables/slv_nonlin.csv b/docs/source/solvers/_tables/slv_nonlin.csv
@@ -9,4 +9,7 @@
 :ref:`loqo`,|y|,|y|,|n|,|n|
 :ref:`minos`,|y|,|y|,|n|,|n|
 :ref:`snopt`,|y|,|y|,|n|,|n|
+:ref:`ipopt`,|y|,|y|,|n|,|n|
+:ref:`bonmin`,|y|,|y|,|y|,|n|
+:ref:`couenne`,|y|,|y|,|y|,|y|
 
diff --git a/docs/source/solvers/bonmin/index.md b/docs/source/solvers/bonmin/index.md
@@ -0,0 +1,106 @@
+(bonmin)=
+
+# BONMIN
+
+The [COIN Bonmin solver (BONMIN)](https://coin-or.github.io/Bonmin/) Bonmin is an experimental open-source C++ code for solving general MINLP (Mixed Integer NonLinear Programming) problems of the form:
+
+```
+   min     f(x)
+
+s.t.       g_L <= g(x) <= g_U
+           x_L <=  x   <= x_U
+           x_i in Z for all i in I and,
+           x_i in R for all i not in I.
+```
+where `f(x): R^n --> R`, `g(x): R^n --> R^m` are twice continuously differentiable functions and `I` is a subset of `{1,..,n}`.
+
+[[Read More](https://ampl.com/products/solvers/open-source-solvers/)]
+[[Options](#solver-options)]
+[[Download BONMIN](https://portal.ampl.com/user/ampl/download/coin)]
+
+## How to use it
+
+```{eval-rst}
+
+.. tabs::
+
+   .. tab:: AMPL
+
+        .. code-block:: ampl
+
+            ampl: option solver bonmin; # change the solver
+            ampl: option bonmin_options 'option1=value1 option2=value2'; # specify options
+            ampl: solve; # solve the problem
+
+   .. tab:: Python
+   
+        How to install using `amplpy <https://amplpy.ampl.com>`_:
+
+        .. code-block:: bash
+
+            # Install Python API for AMPL:
+            $ python -m pip install amplpy --upgrade
+
+            # Install AMPL & solver modules:
+            $ python -m amplpy.modules install coin # install BONMIN
+
+            # Activate your license (e.g., free ampl.com/ce or ampl.com/courses licenses):
+            $ python -m amplpy.modules activate <your-license-uuid>
+
+        How to use:
+
+        .. code-block:: python
+
+            from amplpy import AMPL
+            ampl = AMPL()
+            ...
+            ampl.solve(solver="bonmin", bonmin_options="option1=value1 option2=value2")
+
+        Learn more about what we have to offer to implement and deploy `Optimization in Python <https://ampl.com/python/>`_.
+
+   .. tab:: Other APIs
+
+        `AMPL APIs <https://ampl.com/apis/>`_ are interfaces that allow developers to access the features of the AMPL interpreter from within a programming language. We have APIs available for:
+
+        - `Python <https://ampl.com/api/latest/python>`_
+        - `R <https://ampl.com/api/latest/R>`_
+        - `C++ <https://ampl.com/api/latest/cpp>`_
+        - `C#/.NET <https://ampl.com/api/latest/dotnet>`_
+        - `Java <https://ampl.com/api/latest/java>`_
+        - `MATLAB <https://ampl.com/api/latest/matlab>`_
+```
+
+## Resources
+
+* [Solver options](#solver-options)
+* [Solve result codes](#retrieving-solutions)
+
+## Solver options
+
+Full list of solver options:
+```{toctree}
+options.md
+```
+
+More details on solver options: [Features guide](https://mp.ampl.com/features-guide.html).
+
+
+## Retrieving solutions
+
+The outcome of the last optimization is stored in the AMPL parameter `solve_result_num` and the relative message in
+`solve_result`.
+
+```ampl
+display solve_result_num, solve_result;
+```
+
+BONMIN solve result codes:
+```
+          0- 99 solved: optimal for an optimization problem, feasible for a satisfaction problem
+        100-199 solved? solution candidate returned but error likely
+        200-299 infeasible
+        300-399 unbounded
+        400-499 limit
+        500-999 failure, no solution returned
+```
+
diff --git a/docs/source/solvers/bonmin/options.md b/docs/source/solvers/bonmin/options.md
diff --git a/docs/source/solvers/couenne/index.md b/docs/source/solvers/couenne/index.md
@@ -0,0 +1,96 @@
+(couenne)=
+
+# COUENNE
+
+The [COIN Couenne solver (COUENNE, Convex Over and Under ENvelopes for Nonlinear Estimation)](https://github.com/coin-or/Couenne) is a spatial branch & bound algorithm that implements linearization, bound reduction, and branching techniques for Mixed-integer, Nonlinear Programming (MINLP) problems. The purpose of Couenne is to find global optima of nonconvex MINLPs.
+
+[[Read More](https://ampl.com/products/solvers/open-source-solvers/)]
+[[Options](#solver-options)]
+[[Download COUENNE](https://portal.ampl.com/user/ampl/download/coin)]
+
+## How to use it
+
+```{eval-rst}
+
+.. tabs::
+
+   .. tab:: AMPL
+
+        .. code-block:: ampl
+
+            ampl: option solver couenne; # change the solver
+            ampl: option couenne_options 'option1=value1 option2=value2'; # specify options
+            ampl: solve; # solve the problem
+
+   .. tab:: Python
+   
+        How to install using `amplpy <https://amplpy.ampl.com>`_:
+
+        .. code-block:: bash
+
+            # Install Python API for AMPL:
+            $ python -m pip install amplpy --upgrade
+
+            # Install AMPL & solver modules:
+            $ python -m amplpy.modules install coin # install COUENNE
+
+            # Activate your license (e.g., free ampl.com/ce or ampl.com/courses licenses):
+            $ python -m amplpy.modules activate <your-license-uuid>
+
+        How to use:
+
+        .. code-block:: python
+
+            from amplpy import AMPL
+            ampl = AMPL()
+            ...
+            ampl.solve(solver="couenne", couenne_options="option1=value1 option2=value2")
+
+        Learn more about what we have to offer to implement and deploy `Optimization in Python <https://ampl.com/python/>`_.
+
+   .. tab:: Other APIs
+
+        `AMPL APIs <https://ampl.com/apis/>`_ are interfaces that allow developers to access the features of the AMPL interpreter from within a programming language. We have APIs available for:
+
+        - `Python <https://ampl.com/api/latest/python>`_
+        - `R <https://ampl.com/api/latest/R>`_
+        - `C++ <https://ampl.com/api/latest/cpp>`_
+        - `C#/.NET <https://ampl.com/api/latest/dotnet>`_
+        - `Java <https://ampl.com/api/latest/java>`_
+        - `MATLAB <https://ampl.com/api/latest/matlab>`_
+```
+
+## Resources
+
+* [Solver options](#solver-options)
+* [Solve result codes](#retrieving-solutions)
+
+## Solver options
+
+Full list of solver options:
+```{toctree}
+options.md
+```
+
+More details on solver options: [Features guide](https://mp.ampl.com/features-guide.html).
+
+
+## Retrieving solutions
+
+The outcome of the last optimization is stored in the AMPL parameter `solve_result_num` and the relative message in
+`solve_result`.
+
+```ampl
+display solve_result_num, solve_result;
+```
+
+COUENNE solve result codes:
+```
+          0- 99 solved: optimal for an optimization problem, feasible for a satisfaction problem
+        100-199 solved? solution candidate returned but error likely
+        200-299 infeasible
+        300-399 unbounded
+        400-499 limit
+        500-999 failure, no solution returned
+```
+