CVXPYgen 0.0.1
Code generation with CVXPY
License: Apache License, Version 2.0
Requires Python: >=3.6


# CVXPYgen - Code generation with CVXPY

CVXPYgen takes a convex optimization problem family modeled with CVXPY and generates a corresponding solver in C.
This custom solver is specific to the problem family and accepts different parameter values.
In particular, this solver is suitable for deployment on embedded systems.

CVXPYgen accepts CVXPY problems that are compliant with [Disciplined Convex Programming (DCP)](https://www.cvxpy.org/tutorial/dcp/index.html).
DCP is a system for constructing mathematical expressions with known curvature from a given library of base functions. 
CVXPY uses DCP to ensure that the specified optimization problems are convex.
In addition, problems need to be modeled according to [Disciplined Parametrized Programming (DPP)](https://www.cvxpy.org/tutorial/advanced/index.html#disciplined-parametrized-programming).
Solving a DPP-compliant problem repeatedly for different values of the parameters can be much faster than repeatedly solving a new problem.

For now, CVXPYgen is a separate module, until it will be integrated into CVXPY.
As of today, CVXPYgen works with linear, quadratic, and second-order cone programs.

**Important: When generating code with the ECOS solver, the generated code is licensed 
under the [GNU General Public License v3.0](https://github.com/embotech/ecos/blob/develop/COPYING).**

## Installation

1. Clone this repository via SSH,
    ```
    git clone git@github.com:cvxgrp/codegen.git
    ```
   or via HTTPS,
    ```
    git clone https://github.com/cvxgrp/codegen.git
    ```
   and initialize its submodules.
    ```
    cd codegen
    git submodule update --init
    ```


2. Install [conda](https://docs.conda.io/en/latest/), create and activate the ``cpg_env`` environment.
    ```
    conda env create -f environment.yml
    conda activate cpg_env
    ```
   

3. On Unix, install the [GCC compiler](https://gcc.gnu.org).
   On Windows, install [Microsoft Visual Studio](https://visualstudio.microsoft.com/downloads/) with the 'Desktop development with C++' workload.
   CVXPYgen is tested with Visual Studio 2019 and 2022, older versions might work as well.
   

4. *Optional:* If you wish to use the example notebooks located in ``examples/``, register a new kernel spec with Jupyter.
    ```
   conda activate cpg_env
   ipython kernel install --user --name=cpg_env
   ```
   In the Jupyter notebook, click on ``Kernel->Change kernel`` and choose ``cpg_env``.
    
## Example

We define a simple 'nonnegative least squares' problem, generate code for it, and solve the problem with example parameter values.

### 1. Generate Code

Let's step through the first part of [``examples/main.py``](https://github.com/cvxgrp/cvxpygen/blob/master/examples/main.py) and model a nonnegative least squares problem.
Define a convex optimization problem the way you are used to with CVXPY.
Everything that is described as ``cp.Parameter()`` is assumed to be changing between multiple solves.
For constant properties, use ``cp.Constant()``.

```python
import cvxpy as cp

m, n = 3, 2
x = cp.Variable(n, name='x')
A = cp.Parameter((m, n), name='A', sparsity=[(0, 0), (0, 1), (1, 1)])
b = cp.Parameter(m, name='b')
problem = cp.Problem(cp.Minimize(cp.sum_squares(A @ x - b)), [x >= 0])
```

Specify the `name` attribute for variables and parameters to recognize them after generating code.
The attribute `sparsity` is a list of 2-tuples that indicate the coordinates of nonzero entries of matrix `A`.
Parameter sparsity is only taken into account for matrices.

Assign parameter values and test-solve.

```python
import numpy as np

np.random.seed(0)
A.value = np.zeros((m, n))
A.value[0, 0] = np.random.randn()
A.value[0, 1] = np.random.randn()
A.value[1, 1] = np.random.randn()
b.value = np.random.randn(m)
problem.solve()
```

Generating C code for this problem is as simple as,

```python
from cvxpygen import cpg

cpg.generate_code(problem, code_dir='nonneg_LS', solver='SCS')
```

where the generated code is stored inside `nonneg_LS` and the `SCS` solver is used. 
Next to the positional argument `problem`, all keyword arguments for the `generate_code()` method are summarized below.

| Argument         | Meaning       | Default       |
| -------------    | ------------- | ------------- |
| `code_dir`       | directory for code to be stored in                                 | `'CPG_code'` |
| `solver`         | canonical solver to generate code with                             | CVXPY default |
| `unroll`         | unroll loops in canonicalization code                              | `False` |
| `prefix`         | prefix for unique code symbols when dealing with multiple problems | `''`
| `wrapper`        | compile python wrapper for CVXPY interface                         | `True` |

You can find an overview of the code generation result in `nonneg_LS/README.html`.

### 2. Solve & Compare

As summarized in the second part of [``examples/main.py``](https://github.com/cvxgrp/cvxpygen/blob/master/examples/main.py), after assigning parameter values, you can solve the problem both conventionally and via the generated code, which is wrapped inside the custom CVXPY solve method ``cpg_solve``.

```python
import time

# import extension module and register custom CVXPY solve method
from nonneg_LS.cpg_solver import cpg_solve
problem.register_solve('cpg', cpg_solve)

# solve problem conventionally
t0 = time.time()
val = problem.solve(solver='SCS')
t1 = time.time()
print('\nStandard method\nSolve time:', np.round(1000*(t1-t0), 3), 'ms')
print('Primal solution: x = ', x.value)
print('Dual solution: d0 = ', problem.constraints[0].dual_value)
print('Objective function value:', val)

# solve problem with C code via python wrapper
t0 = time.time()
val = problem.solve(method='cpg', updated_params=['A', 'b'], verbose=False)
t1 = time.time()
print('\nCodegen method\nSolve time:', np.round(1000*(t1-t0), 3), 'ms')
print('Primal solution: x = ', x.value)
print('Dual solution: d0 = ', problem.constraints[0].dual_value)
print('Objective function value:', val)
```

The argument `updated_params` specifies which user-defined parameter values are new.
If the argument is omitted, all parameter values are assumed to be new.
If only a subset of the user-defined parameters have new values, use this argument to speed up the solver.
Most solver settings can be specified as keyword arguments like without code generation. 
Here, we use `verbose=False` to suppress printing.

Comparing the standard and codegen methods for this example, both the solutions and objective values are close.
Especially for smaller problems like this, the new solve method ``'cpg'`` is significantly faster than solving without code generation.

### 3. Executable

In the C code, all of your parameters and variables are stored as vectors via Fortran-style flattening (vertical index moves fastest).
For example, the `(i, j)`-th entry of the original matrix with height `h` will be the `i+j*h`-th entry of the flattened matrix in C.
For sparse *parameters*, i.e. matrices, the `k`-th entry of the C array is the `k`-th nonzero entry encountered when proceeding
through the parameter column by column.

Before compiling the example executable, remember to activate the `cpg_env` environment.

```
conda activate cpg_env
```

On Unix platforms, run the following commands in your terminal to compile and run the program:

```bash
cd nonneg_LS/c/build
cmake ..
cmake --build . --target cpg_example
./cpg_example
```

On Windows, type:

```bash
cd nonneg_LS\c\build
cmake ..
cmake --build . --target cpg_example --config release
Release\cpg_example
```

## Tests

To run tests, install ``pytest`` via

```bash
conda install pytest
```

and execute:

```bash
cd tests
pytest
```