optimization_engine 0.12.0

A pure Rust framework for embedded nonconvex optimization. Ideal for robotics!
Documentation
use super::Constraint;
use super::Simplex;
use crate::FunctionCallResult;
use num::Float;
use std::iter::Sum;

#[derive(Copy, Clone)]
/// A norm-1 ball, that is, a set given by $B_1^r = \\{x \in \mathbb{R}^n {}:{} \Vert{}x{}\Vert_1 \leq r\\}$
/// or a ball-1 centered at a point $x_c$, that is, $B_1^{x_c, r} = \\{x \in \mathbb{R}^n {}:{} \Vert{}x-x_c{}\Vert_1 \leq r\\}$
pub struct Ball1<'a, T = f64> {
    center: Option<&'a [T]>,
    radius: T,
    simplex: Simplex<T>,
}

impl<'a, T: Float> Ball1<'a, T> {
    /// Construct a new ball-1 with given center and radius.
    /// If no `center` is given, then it is assumed to be in the origin
    ///
    /// # Example
    ///
    /// ```
    /// use optimization_engine::constraints::{Ball1, Constraint};
    ///
    /// let ball = Ball1::new(None, 1.0);
    /// let mut x = [2.0, -0.5];
    /// ball.project(&mut x).unwrap();
    /// ```
    pub fn new(center: Option<&'a [T]>, radius: T) -> Self {
        assert!(radius > T::zero());
        let simplex = Simplex::new(radius);
        Ball1 {
            center,
            radius,
            simplex,
        }
    }

    fn project_on_ball1_centered_at_origin(&self, x: &mut [T]) -> FunctionCallResult
    where
        T: Sum<T>,
    {
        if crate::matrix_operations::norm1(x) > self.radius {
            // u = |x| (copied)
            let mut u = vec![T::zero(); x.len()];
            u.iter_mut()
                .zip(x.iter())
                .for_each(|(ui, &xi)| *ui = xi.abs());
            // u = P_simplex(u)
            self.simplex.project(&mut u)?;
            x.iter_mut()
                .zip(u.iter())
                .for_each(|(xi, &ui)| *xi = xi.signum() * ui);
        }
        Ok(())
    }
}

impl<'a, T> Constraint<T> for Ball1<'a, T>
where
    T: Float + Sum<T>,
{
    fn project(&self, x: &mut [T]) -> FunctionCallResult {
        if let Some(center) = &self.center {
            assert_eq!(
                x.len(),
                center.len(),
                "x and xc have incompatible dimensions"
            );
            x.iter_mut()
                .zip(center.iter())
                .for_each(|(xi, &ci)| *xi = *xi - ci);
            self.project_on_ball1_centered_at_origin(x)?;
            x.iter_mut()
                .zip(center.iter())
                .for_each(|(xi, &ci)| *xi = *xi + ci);
        } else {
            self.project_on_ball1_centered_at_origin(x)?;
        }
        Ok(())
    }

    fn is_convex(&self) -> bool {
        true
    }
}