optimization_engine/constraints/
ball2.rs

1use super::Constraint;
2
3#[derive(Copy, Clone)]
4/// A Euclidean ball, that is, a set given by $B_2^r = \\{x \in \mathbb{R}^n {}:{} \Vert{}x{}\Vert \leq r\\}$
5/// or a Euclidean ball centered at a point $x_c$, that is, $B_2^{x_c, r} = \\{x \in \mathbb{R}^n {}:{} \Vert{}x-x_c{}\Vert \leq r\\}$
6pub struct Ball2<'a> {
7    center: Option<&'a [f64]>,
8    radius: f64,
9}
10
11impl<'a> Ball2<'a> {
12    /// Construct a new Euclidean ball with given center and radius
13    /// If no `center` is given, then it is assumed to be in the origin
14    pub fn new(center: Option<&'a [f64]>, radius: f64) -> Self {
15        assert!(radius > 0.0);
16
17        Ball2 { center, radius }
18    }
19}
20
21impl<'a> Constraint for Ball2<'a> {
22    fn project(&self, x: &mut [f64]) {
23        if let Some(center) = &self.center {
24            assert_eq!(
25                x.len(),
26                center.len(),
27                "x and xc have incompatible dimensions"
28            );
29            let mut norm_difference = 0.0;
30            x.iter().zip(center.iter()).for_each(|(a, b)| {
31                let diff_ = *a - *b;
32                norm_difference += diff_ * diff_
33            });
34
35            norm_difference = norm_difference.sqrt();
36
37            if norm_difference > self.radius {
38                x.iter_mut().zip(center.iter()).for_each(|(x, c)| {
39                    *x = *c + self.radius * (*x - *c) / norm_difference;
40                });
41            }
42        } else {
43            let norm_x = crate::matrix_operations::norm2(x);
44            if norm_x > self.radius {
45                let norm_over_radius = norm_x / self.radius;
46                x.iter_mut().for_each(|x_| *x_ /= norm_over_radius);
47            }
48        }
49    }
50
51    fn is_convex(&self) -> bool {
52        true
53    }
54}
optimization_engine/constraints/ball2.rs

optimization_engine/constraints/
ball2.rs
