Struct optimization_engine::constraints::FiniteSet

pub struct FiniteSet<'a> { /* private fields */ }

A finite set, $X = \{x_1, x_2, \ldots, x_n\}\subseteq\mathbb{R}^n$, given vectors $x_i\in\mathbb{R}^n$

Implementations

impl<'a> FiniteSet<'a>

pub fn new(data: &'a [&'a [f64]]) -> Self

Construct a finite set, $X = \{x_1, x_2, \ldots, x_n\}$, given vectors $x_i\in\mathbb{R}^n$

Arguments

• data: vector of vectors (see example below)

Example

use optimization_engine::constraints::{Constraint, FiniteSet};

let data: &[&[f64]] = &[
   &[1.0, 1.0],
   &[0.0, 1.0],
   &[1.0, 0.0],
   &[0.0, 0.0],
];
let finite_set = FiniteSet::new(data);

Panics

This method will panic if (i) the given vector of data is empty and (ii) if the given vectors have unequal dimensions.

Trait Implementations

impl<'a> Clone for FiniteSet<'a>

fn clone(&self) -> FiniteSet<'a>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<'a> Constraint for FiniteSet<'a>

fn project(&self, x: &mut [f64])

Projection on the current finite set

Traverses the elements of the vector, computes norm-2 distances to each element, and updates the given vector x with the closest element from the finite set.

Parameters

• x: (input) given vector, (output) projection on finite set

Example

use optimization_engine::constraints::*;

let data: &[&[f64]] = &[
   &[0.0, 0.0],
   &[1.0, 1.0],
];
let finite_set = FiniteSet::new(data);
let mut x = [0.7, 0.6];
finite_set.project(&mut x); // compute projection

Panics

Does not panic

fn is_convex(&self) -> bool

Returns true if and only if the set is convex

impl<'a> Copy for FiniteSet<'a>