Struct Clustering 

pub struct Clustering { /* private fields */ }

The Clustering problem.

Given a set of n elements with pairwise distances, a cluster count bound K, and a diameter bound B, determine whether there exists a partition of the elements into at most K non-empty clusters such that for every cluster, all pairwise distances within that cluster are at most B.

Representation

Each element i is assigned a cluster index config[i] ∈ {0, ..., K-1}. The problem is satisfiable iff every non-empty cluster has all pairwise distances ≤ B.

Example

use problemreductions::models::misc::Clustering;
use problemreductions::{Problem, Solver, BruteForce};

// 4 elements, 2 clusters, diameter bound 1
let distances = vec![
    vec![0, 1, 3, 3],
    vec![1, 0, 3, 3],
    vec![3, 3, 0, 1],
    vec![3, 3, 1, 0],
];
let problem = Clustering::new(distances, 2, 1);
let solver = BruteForce::new();
let solution = solver.find_witness(&problem);
assert!(solution.is_some());

Implementations

impl Clustering

pub fn new( distances: Vec<Vec<u64>>, num_clusters: usize, diameter_bound: u64, ) -> Self

Create a new Clustering instance.

Panics

Panics if:

• distances is empty
• distances is not square
• distances is not symmetric
• diagonal entries are not zero
• num_clusters is zero

pub fn distances(&self) -> &[Vec<u64>]

Returns the distance matrix.

pub fn num_elements(&self) -> usize

Returns the number of elements.

pub fn num_clusters(&self) -> usize

Returns the maximum number of clusters K.

pub fn diameter_bound(&self) -> u64

Returns the diameter bound B.

Trait Implementations

impl Clone for Clustering

fn clone(&self) -> Clustering

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Clustering

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Clustering

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Problem for Clustering

const NAME: &'static str = "Clustering"

Base name of this problem type (e.g., “MaximumIndependentSet”).

type Value = Or

The evaluation value type.

fn variant() -> Vec<(&'static str, &'static str)>

Returns variant attributes derived from type parameters.

fn dims(&self) -> Vec<usize>

Configuration space dimensions. Each entry is the cardinality of that variable.

fn evaluate(&self, config: &[usize]) -> Or

Evaluate the problem on a configuration.

fn num_variables(&self) -> usize

Number of variables (derived from dims).

fn problem_type() -> ProblemType

Look up this problem’s catalog entry.

impl ReduceTo<Clustering> for KColoring<K3, SimpleGraph>

type Result = ReductionKColoringToClustering

The reduction result type.

fn reduce_to(&self) -> Self::Result

Reduce this problem to the target problem type.

impl ReduceTo<ILP> for Clustering

type Result = ReductionClusteringToILP

The reduction result type.

fn reduce_to(&self) -> Self::Result

Reduce this problem to the target problem type.

impl Serialize for Clustering

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl DeclaredVariant for Clustering