Getting started#

Overview#

PySolverView: An Interface for SciPy Solvers#

PySolverView is a wrapper for the equation and optimization solvers of scipy.

scipy.optimize.root, is a solver for nonlinear systems of equations:

\[F(x) = 0\]

Where \(F: \mathbb{R}^n \rightarrow \mathbb{R}^n\) is a vector-valued function of the vector \(x\).

scipy.optimize.minimize is a solver for optimization problems with the following structure:

\[\begin{split}\begin{align} \text{minimize} \quad & f(\mathbf{x}) \; \text{with} \; \mathbf{x} \in \mathbb{R}^n \\ \text{s.t.} \quad & c_{\mathrm{eq}}(\mathbf{x}) = 0 \\ & c_{\mathrm{in}}(\mathbf{x}) \geq 0 \\ & \mathbf{x}_l \leq \mathbf{x} \leq \mathbf{x}_u \end{align}\end{split}\]

Where:

\(\mathbf{x}\) represents the vector of decision variables (i.e., degree of freedom).
\(f(\mathbf{x})\) is the objective function to minimize.
\(c_{\mathrm{eq}}(\mathbf{x})\) are the problem’s equality constraints.
\(c_{\mathrm{in}}(\mathbf{x})\) are the problem’s inequality constraints.
\(\mathbf{x}_l\) and \(\mathbf{x}_u\) are the lower and upper bounds on the decision variables, respectively.

Main Features of PySolverView#

PySolverView is a wrapper for the solvers of the Scipy package. Using this interface brings some advantages with respect to the vanilla Scipy experience:

Monitoring Capabilities:
- Display convergence progress.
- Write convergence history to log file.
- Real-time plotting of solution progression.
Problem Definition:
- Leverage templated object to streamlined problem definition.
- Evaluate objective function and constraints simultaneously to avoid redundant calculations.
Efficient Evaluations with Caching Mechanism:
PySolverView integrates a caching mechanism. In cases where objective functions, equality constraints, and inequality constraints all require evaluations, the mechanism checks for unchanged independent variable seta and uses of previous calculations to avoid redundant recalculations.

User Installation Guide#

This guide will walk you through the process of installing pysolver_view via pip. To isolate the installation and avoid conflicts with other Python packages, it is recommended to create a dedicated Conda virtual environment.

Ensure conda is installed:

Check if conda is installed in your terminal:
```
conda list
```
If installed packages do not appear, install conda.
Open a terminal or command prompt and create a new virtual environment named pysolver_env:
```
conda create --name pysolver_env python=3.11
```
Activate the newly created virtual environment:
```
conda activate pysolver_env
```
Install the package using pip within the activated virtual environment:
```
pip install pysolver_view
```
Verify the installation by running some of the examples in the [demos](../../demos) directory

Note

By default, pysolver_view can use the optimization solvers available in the scipy package. However, a wider range of solvers are available through the pygmo wrapper, including IPOPT and SNOPT.

conda install -c conda-forge pygmo
conda install -c conda-forge pygmo_plugins_nonfree

Developer Installation Guide#

This installation guide is intended for developers who wish to contribute to or modify the pysolver_view source code. It assumes that the developer is using a Linux distribution or Windows with Git Bash terminal to have access to Git and Linux-like commands.

Fork the repository:

Navigate to the project’s GitHub page <https://github.com/turbo-sim/pysolver_view> and click the “Fork” button in the upper right corner of the repository page to create a copy of the repository under your own GitHub account.
Clone the forked repository:

Open your terminal and run the following command, replacing <your-username> with your GitHub username:
```
git clone https://github.com/<your-username>/<repository-name>.git
```
Navigate into the cloned repository:
```
cd <repository-name>
```
Create a dedicated Conda virtual environment for development:

Check that conda is installed:
```
conda list
```
If conda is not installed, install conda.

Create dedicated virtual environment for the package:
```
conda env create --file environment.yaml
```
Activate the newly created virtual environment:
```
conda activate pysolver_env
```
Use Poetry to install the required dependencies along with the package for local development:
```
poetry install
```