diff --git a/.gitignore b/.gitignore index b829c8f6..60581a6e 100644 --- a/.gitignore +++ b/.gitignore @@ -36,7 +36,7 @@ open-clib/Cargo.lock *.rs.bk # Docusaurus website (autogenerated files) -/website/ +/website/build/ !/website/pages/ !/website/static/ !/website/*.js diff --git a/docs/installation.md b/docs/installation.md index 55b3dc95..a0a3c620 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -16,12 +16,12 @@ description: How to install OpEn Before you start, you need to install -* **Rust**, following the official [installation guide](https://www.rust-lang.org/tools/install), +* **Rust**, following the official installation guide, - Why? The Rust compiler is an essential component of OpEn; you will most likely not need to write (or compile yourself) any Rust code, but OpEn's Python/MATLAB interface will need the compiler to build your optimizer -* **clang**, following this [guide](https://github.com/rust-lang/rust-bindgen/blob/master/book/src/requirements.md) - - Why? OpEn uses CasADi to build certain functions in C, which then need to be +* **clang**, following this guide + - Why? OpEn uses CasADi to build certain functions in C, which then need to be called from OpEn's core solver in Rust. For that purpose we need **bindgen**, which requires **clang** @@ -36,13 +36,13 @@ pip install opengen You might need to prepend `sudo` on some Linux systems. Note that OpEn requires Python **version 3.5 or newer**. You might, therefore, -need to install it using +need to install it using ```console pip3 install opengen ``` -OpEn may run on earlier versions of Python (as old as 2.7), but we cannot promise +OpEn may run on earlier versions of Python (as old as 2.7), but we cannot promise you that (the main difficulty being the installation of dependencies). ### Python installation with virtualenv @@ -58,7 +58,7 @@ pip install opengen ``` ## MATLAB Interface -You first need to download [Optimization Engine](https://github.com/alphaville/optimization-engine/archive/master.zip), `cd` to `./matlab/` and run +You first need to download [Optimization Engine](https://github.com/alphaville/optimization-engine/archive/master.zip), `cd` to `./matlab/` and run ```matlab setup_open @@ -66,9 +66,9 @@ setup_open This will include to your MATLAB path all necessary folders. -You also need to [**download and install CasADi**](https://web.casadi.org/). +You also need to download and install CasADi. -## OpEn in Rust +## OpEn in Rust To use **OpEn** in your Rust project, add the following in your project's `Cargo.toml` file: ``` @@ -77,4 +77,4 @@ optimization_engine = "*" ``` You may replace the asterisk with some particular version (e.g., `optimization_engine = "0.6.0"`). -**OpEn** is available on [crates.io](https://crates.io/crates/optimization_engine). +**OpEn** is available on crates.io. diff --git a/website/pages/en/index.js b/website/pages/en/index.js index 2776543a..f05bd377 100755 --- a/website/pages/en/index.js +++ b/website/pages/en/index.js @@ -14,7 +14,7 @@ const { MarkdownBlock, GridBlock, Container } = CompLibrary; /* Used to read mar const siteConfig = require(`${process.cwd()}/siteConfig.js`); function docUrl(doc, language) { - return `${siteConfig.baseUrl}${language ? `${language}/` : ""}${doc}`; + return `${siteConfig.baseUrl}docs/${doc}`; } function imgUrl(img) { @@ -81,7 +81,7 @@ class HomeSplash extends React.Component {
Moving Horizon Estimation (MHE) is the bee's knees of nonlinear estimation: it is an optimization-based estimator for constrained nonlinear systems. MHE is backed by a strong theoretical bedrock that combines maximum likelihood estimation and dynamic programming; however, its applicability has been hampered by the associated computational burden and has limited its use to slow or linear dynamical systems. OpEn can unlock the huge potential of MHE and facilitate its use in robotics, automotive, aerospace and other applications with high sampling frequencies.
Moving Horizon Estimation (MHE) is the bee's knees of nonlinear estimation: it is an optimization-based estimator for constrained nonlinear systems. MHE is backed by a strong theoretical bedrock that combines Bayesian estimation and dynamic programming; however, its applicability has been hampered by the associated computational burden and has limited its use to slow or linear dynamical systems. OpEn can unlock the huge potential of MHE and facilitate its use in robotics, automotive, aerospace and other applications with high sampling frequencies.