mcx · pull · Mar 21, 2025 · Mar 21, 2025
diff --git a/docs/codegen/python.rst b/docs/codegen/python.rst
@@ -35,31 +35,28 @@ Additional codegen options are shown in the following table
 || :code:`printing_enable`     || Enable printing                                  || :code:`False` (default)     |
 ||                             ||                                                  || :code:`True`                |
 +------------------------------+---------------------------------------------------+------------------------------+
-|| Enable profiling            || Enable profiling                                 || :code:`False` (default)     |
+|| :code:`profiling_enable`    || Enable profiling                                 || :code:`False` (default)     |
 ||                             ||                                                  || :code:`True`                |
 +------------------------------+---------------------------------------------------+------------------------------+
-|| Enable interrupts           || Enable interrupts                                || :code:`False` (default)     |
+|| :code:`interrupt_enable`    || Enable interrupts                                || :code:`False` (default)     |
 ||                             ||                                                  || :code:`True`                |
 +------------------------------+---------------------------------------------------+------------------------------+
-|| :code:`include_codegen_src` || Include codegen source code                      || :code:`True` (default)      |
-||                             ||                                                  || :code:`False`               |
+|| :code:`include_codegen_src` || Include codegen source code (this includes       || :code:`True` (default)      |
+||                             || headers/sources/Makefile in the output folder,   || :code:`False`               |
+||                             || creating a self-contained compilable folder.     ||                             |
 +------------------------------+---------------------------------------------------+------------------------------+
-|| :code:`prefix`              || Prefix to add                                    || :code:`''` (default)        |
-||                             ||                                                  || string                      |
+|| :code:`prefix`              || Prefix for filenames and C variables,            || :code:`''` (default)        |
+||                             || useful if generating multiple problems.          || string                      |
 +------------------------------+---------------------------------------------------+------------------------------+
-|| :code:`compile`             || Compile the code                                 || :code:`False` (default)     |
-||                             ||                                                  || :code:`True`                |
+|| :code:`compile`             || Compile the above python extension into an       || :code:`False` (default)     |
+||                             || importable module, allowing "import emosqp"      || :code:`True`                |
 +------------------------------+---------------------------------------------------+------------------------------+
 
 The options are passed using named arguments, e.g.,
 
 .. code:: python
 
-    m.codegen('code', parameters='matrices', extension_name='emosqp')
-
-If the :code:`project_type` argument is not passed or is set to :code:`''`,
-then no build files are generated.
-
+    m.codegen('code', parameters='matrices', extension_name='myosqpext')
 
 
 Extension module API
@@ -68,9 +65,9 @@ Once the code is generated, you can import a light python wrapper with
 
 .. code:: python
 
-    import emosqp
+    import myosqpext
 
-where :code:`emosqp` is the extension name given in the previous section. The module imports the following functions
+where :code:`myosqpext` is the extension name given in the previous section. The module imports the following functions
 
 .. py:function:: solve()
    :noindex:
@@ -171,4 +168,4 @@ You can update all the nonzero entries in matrix :math:`A` by running
 
 .. code:: python
 
-    emosqp.update_A(Ax_new, None, 0);
+    myosqpext.update_A(Ax_new, None, 0);
diff --git a/docs/conf.py b/docs/conf.py
@@ -58,7 +58,7 @@
 
 # General information about the project.
 project = 'OSQP'
-copyright = '2021, Bartolomeo Stellato, Goran Banjac'
+copyright = '2025, OSQP Developer Team'
 author = 'Bartolomeo Stellato, Goran Banjac'
 
 # The version info for the project you're documenting, acts as replacement for

diff --git a/docs/contributing/python.rst b/docs/contributing/python.rst
@@ -14,102 +14,8 @@ check what algebra backends we have successfully built.
 
         $ python -c "from osqp import algebras_available; print(algebras_available())"
 
-Wrappers for ``builtin`` algebra
---------------------------------
-
-Building wrappers for the ``builtin`` algebra demonstrates some of the basic steps
-you would need to take for all algebra backends.
-
-.. code-block:: bash
-
-	$ conda create --name osqp_build python=3.9
-	$ conda activate osqp_build
-
-	# "build" is the python package that is capable of generating binary "wheels"
-	# there are other ways of doing this but this way is the most standards compliant
-	$ pip install build
-
-	$ git clone [email protected]:osqp/osqp-python.git
-	$ cd osqp-python/
-
-	# Build the python wheels for "builtin" algebra
-	$ python -m build .
-
-	# Built artifacts are in the "dist" folder, we can "pip install" any of these files
-	# installing the wheel entails no (further) compilation so its fastest
-	$ tree dist
-	dist
-	├── osqp-1.0.0b1-cp39-cp39-linux_x86_64.whl    <- compiled wheel
-	└── osqp-1.0.0b1.tar.gz                        <- source gz
-
-	$ pip install dist/osqp-1.0.0b1-cp39-cp39-linux_x86_64.whl
-
-	$ python -c "from osqp import algebras_available; print(algebras_available())"
-	['builtin']
-
-Wrappers for ``mkl`` algebra
-----------------------------
-
-For building Python wrappers for the ``MKL`` algebra backend, you need to have the `Intel OneAPI <https://www.intel.com/content/www/us/en/developer/tools/oneapi/toolkits.html#base-kit>`_.
-library installed and accessible for your platform.
-
-Once installed, set the environment variable ``MKL_ROOT`` to the path of the installed toolkit. For example:
-
-.. code-block:: bash
-
-        $ export MKL_ROOT=/opt/intel/oneapi/mkl/latest
-
-The process of building the ``MKL`` OSQP wrappers is the same as above, except that the environment variable
-``OSQP_ALGEBRA_BACKEND`` set to ``mkl`` tells the build process that we wish to build MKL wrappers.
-
-.. code-block:: bash
-
-        $ export OSQP_ALGEBRA_BACKEND=mkl 
-
-	$ python -m build .
-
-	$ tree dist
-	dist
-	├── osqp-1.0.0b1-cp39-cp39-linux_x86_64.whl
-	├── osqp-1.0.0b1.tar.gz
-	├── osqp_mkl-1.0.0b1-cp39-cp39-linux_x86_64.whl
-	└── osqp_mkl-1.0.0b1.tar.gz
-
-	$ pip install dist/osqp_mkl-1.0.0b1-cp39-cp39-linux_x86_64.whl
-	$ python -c "from osqp import algebras_available; print(algebras_available())"
-	['mkl', 'builtin']
-
-Wrappers for ``cuda`` algebra
------------------------------
-
-For building Python wrappers for the ``CUDA`` algebra backend, you need to have the `CUDA Toolkit <https://developer.nvidia.com/cuda-toolkit-archive>`_.
-installed and accessible for your platform. The build process has been tested out for CUDA Toolkit versions 11.2 through 11.7.
-
-If you install the CUDA Toolkit using the default paths and options (which makes the toolkit available at ``/usr/local/cuda`` for \*nix platforms),
-there is no need for any further configuration.
-
-The process of building the ``CUDA`` OSQP wrappers is the same as above, except that the environment variable
-``OSQP_ALGEBRA_BACKEND`` set to ``cuda`` tells the build process that we wish to build CUDA wrappers.
-
-.. code-block:: bash
-
-        $ export OSQP_ALGEBRA_BACKEND=cuda
-
-	$ python -m build .
-
-        $ tree dist
-	dist
-	├── osqp-1.0.0b1-cp39-cp39-linux_x86_64.whl
-	├── osqp-1.0.0b1.tar.gz
-	├── osqp_cuda-1.0.0b1-cp39-cp39-linux_x86_64.whl
-	├── osqp_cuda-1.0.0b1.tar.gz
-	├── osqp_mkl-1.0.0b1-cp39-cp39-linux_x86_64.whl
-	└── osqp_mkl-1.0.0b1.tar.gz
-
-	$ pip install dist/osqp_cuda-1.0.0b1-cp39-cp39-linux_x86_64.whl
-	$ python -c "from osqp import algebras_available; print(algebras_available())"
-	['cuda', 'mkl', 'builtin']
-
+See :ref:`Building alternative algebra backends <python_build_algebras>` on how to build and test
+different OSQP algebra backends.
 
 Building wrappers against an experimental branch of OSQP
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

diff --git a/docs/examples/setup-and-solve.rst b/docs/examples/setup-and-solve.rst
@@ -171,3 +171,38 @@ C
 
         return (int)exitflag;
     };
+
+
+
+Rust
+----
+
+.. code:: rust
+
+    use osqp::{CscMatrix, Problem, Settings};
+
+    fn main() {
+        // Define problem data
+        let P = &[[4.0, 1.0],
+            [1.0, 2.0]];
+        let q = &[1.0, 1.0];
+        let A = &[[1.0, 1.0],
+            [1.0, 0.0],
+            [0.0, 1.0]];
+        let l = &[1.0, 0.0, 0.0];
+        let u = &[1.0, 0.7, 0.7];
+
+        // Extract the upper triangular elements of `P`
+        let P = CscMatrix::from(P).into_upper_tri();
+
+        let settings = Settings::default();
+
+        // Create an OSQP problem
+        let mut prob = Problem::new(P, q, A, l, u, &settings).expect("failed to setup problem");
+
+        // Solve problem
+        let result = prob.solve();
+
+        // Print the solution
+        println!("{:?}", result.x().expect("failed to solve problem"));
+    }
diff --git a/docs/examples/update-matrices.rst b/docs/examples/update-matrices.rst
@@ -230,3 +230,48 @@ C
 
         return (int)exitflag;
     };
+
+
+
+Rust
+----
+
+.. code:: rust
+
+    use osqp::{CscMatrix, Problem, Settings};
+
+    fn main() {
+        // Define problem data
+        let P = &[[4.0, 1.0],
+            [1.0, 2.0]];
+        let q = &[1.0, 1.0];
+        let A = &[[1.0, 1.0],
+            [1.0, 0.0],
+            [0.0, 1.0]];
+        let l = &[1.0, 0.0, 0.0];
+        let u = &[1.0, 0.7, 0.7];
+
+        // Extract the upper triangular elements of `P`
+        let P = CscMatrix::from(P).into_upper_tri();
+
+        let settings = Settings::default();
+
+        // Create an OSQP problem
+        let mut prob = Problem::new(P, q, A, l, u, &settings).expect("failed to setup problem");
+
+        // Solve problem
+        let result = prob.solve();
+
+        // Update P/A without changing their sparsity structure
+        // NB: Update only upper triangular part of P
+        prob.update_P_A(
+            &CscMatrix::from(&[[5.0, 1.5], [1.5, 1.0]]).into_upper_tri(),
+            &[[1.2, 1.1], [1.5, 0.0], [0.0, 0.8]],
+        );
+
+        // Solve updated problem
+        let result = prob.solve();
+
+        // Print the solution
+        println!("{:?}", result.x().expect("failed to solve problem"));
+    }
diff --git a/docs/examples/update-vectors.rst b/docs/examples/update-vectors.rst
@@ -222,3 +222,45 @@ C
 
         return (int)exitflag;
     };
+
+
+
+Rust
+----
+
+.. code:: rust
+
+    use osqp::{CscMatrix, Problem, Settings};
+
+    fn main() {
+        // Define problem data
+        let P = &[[4.0, 1.0],
+            [1.0, 2.0]];
+        let q = &[1.0, 1.0];
+        let A = &[[1.0, 1.0],
+            [1.0, 0.0],
+            [0.0, 1.0]];
+        let l = &[1.0, 0.0, 0.0];
+        let u = &[1.0, 0.7, 0.7];
+
+        // Extract the upper triangular elements of `P`
+        let P = CscMatrix::from(P).into_upper_tri();
+
+        let settings = Settings::default();
+
+        // Create an OSQP problem
+        let mut prob = Problem::new(P, q, A, l, u, &settings).expect("failed to setup problem");
+
+        // Solve problem
+        let result = prob.solve();
+
+        // Update problem
+        prob.update_lin_cost(&[2.0, 3.0]);
+        prob.update_bounds(&[2.0, -1.0, -1.0], &[2.0, 2.5, 2.5]);
+
+        // Solve updated problem
+        let result = prob.solve();
+
+        // Print the solution
+        println!("{:?}", result.x().expect("failed to solve problem"));
+    }
diff --git a/docs/get_started/C.rst b/docs/get_started/C.rst
@@ -9,7 +9,7 @@ Binaries
 Precompiled platform-dependent shared and static libraries are available on `GitHub <https://github.com/osqp/osqp/releases>`__.
 We here assume that the user uncompressed each archive to :code:`OSQP_FOLDER`.
 
-Each archive contains static :code:`OSQP_FOLDER/lib/libosqp.a` and shared :code:`OSQP_FOLDER/lib/libosqp.ext` libraries to be used to interface OSQP to any C/C++ software.
+Each archive contains static :code:`OSQP_FOLDER/lib/libosqpstatic.a` and shared :code:`OSQP_FOLDER/lib/libosqp.ext` libraries to be used to interface OSQP to any C/C++ software.
 The extension :code:`.ext` is platform dependent and is :code:`.so` for Linux, :code:`.dylib` for Mac and :code:`.dll` for Windows.
 The required include files can be found in :code:`OSQP_FOLDER/include`.
 
@@ -40,4 +40,14 @@ If you compiled OSQP from sources and followed the CMake installation instructio
    # Link the OSQP static library
    target_link_libraries(yourTarget PRIVATE osqp::osqpstatic)
 
+You will need to help CMake out by specifying the path to the folder where you installed the OSQP library, for example:
 
+.. code:: bash
+
+   cmake --build . -Dosqp_ROOT=myfolder
+
+You may or may not need to link your target to the standard math library in your :code:`CMakeLists.txt` file:
+
+.. code::
+
+    target_link_libraries(yourTarget PRIVATE m)
diff --git a/docs/get_started/linear_system_solvers.rst b/docs/get_started/linear_system_solvers.rst
@@ -35,7 +35,7 @@ However, it becomes not really efficient for large scale problems since it is no
 
 
 oneMKL Pardiso
------------
+--------------
 `oneMKL Pardiso <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-0/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>`_ is an efficient multi-threaded linear system solver that works well for large scale problems part of the Intel Math Kernel Library.
 Intel offers `free licenses <https://www.intel.com/content/www/us/en/developer/articles/tool/onemkl-license-faq.html>`_ for oneMKL for most non-commercial applications.
 

diff --git a/docs/get_started/matlab.rst b/docs/get_started/matlab.rst
@@ -41,20 +41,14 @@ After you install both, check that your compiler is selected by executing
       setenv('MW_MINGW64_LOC', 'C:\TDM-GCC-64')
 
 
-   where :code:`C:\TDM-GCC-64` is the installation folder for TDM-GCC.
+   where :code:`C:\\TDM-GCC-64` is the installation folder for TDM-GCC.
 
 You can now build the interface by running inside Matlab
 
 .. code:: matlab
 
    !git clone --recurse-submodules https://github.com/osqp/osqp-matlab
-   cd osqp-matlab
-   make_osqp
+   osqp.build('osqp_mex')
 
 
-Then you can add the interface to the search path by executing from the same directory
-
-.. code:: matlab
-
-   addpath(pwd)
-   savepath
+You are now ready to start using the Matlab interface.
-Original file line number
+Diff line change
@@ Expand Up @@
     oneMKL Pardiso
-    -----------
+    --------------
     `oneMKL Pardiso <https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2023-0/onemkl-pardiso-parallel-direct-sparse-solver-iface.html>`_ is an efficient multi-threaded linear system solver that works well for large scale problems part of the Intel Math Kernel Library.
     Intel offers `free licenses <https://www.intel.com/content/www/us/en/developer/articles/tool/onemkl-license-faq.html>`_ for oneMKL for most non-commercial applications.
@@ Expand Down @@