Introduction

odesign is an optimal design of experiments library written in pure rust. It allows to find optimal designs for (quasi-) linear models considering arbitrary optimalities.

This book serves as a high level introduction and theoretical background.

Please find more resources here:

• docs on docs.rs/odesign
• project on sourcehut sr.ht/~maunke/odesign
• read-only mirror on github maunke/odesign

Use Cases

• Fast calculation of optimal designs of arbitrary linear models with custom design bounds and optimalities.
• Research in area of optimal designs; e.g. I am working on a new optimal design feature selection algorithm, a mixture of SFFS, D-, C- and Measurements-Costs-Optimality, allowing to perform model feature selection and measurements alternating.

Core Features

The library consists of three main features:

1. Feature derive
2. Arbitrary optimalities
3. Optimal design solver

Basic example

In short, this is a basic example of an optimal design of the simple polynomial 1 + x within design bounds [-1, +1] and 101 equally distributed grid points as an init design.

use odesign::{
    DOptimality, DualNum, Feature, FeatureFunction, FeatureSet, LinearModel, OptimalDesign, Result,
    SVector, Vector1,
};

#[derive(Feature)]
#[dimension = 1]
struct Monomial {
    i: i32,
}

impl FeatureFunction<1> for Monomial {
    fn f<D: DualNum<f64>>(&self, x: &SVector<D, 1>) -> D {
        x[0].powi(self.i)
    }
}

// f(x): 1 + x
fn main() -> Result<()> {
    let mut fs = FeatureSet::new();
    fs.push(Monomial { i: 0 });
    fs.push(Monomial { i: 1 });

    let lm = LinearModel::from(fs);

    let optimality = DOptimality::new(lm);
    let lower = Vector1::new(-1.0);
    let upper = Vector1::new(1.0);
    let q = Vector1::new(101);

    let mut od = OptimalDesign::new()
        .with_optimality(optimality)
        .with_bound_args(lower, upper)?
        .with_init_design_grid_args(lower, upper, q)?;
    od.solve();

    println!("{od}");

    Ok(())
}
// Output
// ---------- Design ----------
// Weight  Support Vector
// 0.5000  [ -1.0000 ]
// 0.5000  [ +1.0000 ]
// -------- Statistics --------
// Optimality measure: 1.000000
// No. support vectors: 2
// Iterations: 1
// ----------------------------