Struct MaximumLikelihoodRanking 

pub struct MaximumLikelihoodRanking { /* private fields */ }

The Maximum Likelihood Ranking problem.

Given an n x n antisymmetric comparison matrix A where a_ij + a_ji = c (constant) for every pair and a_ii = 0, find a permutation pi that minimizes the total disagreement cost: sum_{i > j} a_{pi(i), pi(j)}. Entries may be negative (e.g. c = 0 gives a skew-symmetric matrix).

Each item is assigned a rank position (0-indexed). The configuration maps item -> rank: config[item] = rank. The permutation pi maps rank -> item (the inverse of config).

Example

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

let matrix = vec![
    vec![0, 4, 3, 5],
    vec![1, 0, 4, 3],
    vec![2, 1, 0, 4],
    vec![0, 2, 1, 0],
];
let problem = MaximumLikelihoodRanking::new(matrix);
let solver = BruteForce::new();
let solution = solver.find_witness(&problem);
assert!(solution.is_some());

Implementations

impl MaximumLikelihoodRanking

pub fn new(matrix: Vec<Vec<i32>>) -> Self

Create a new MaximumLikelihoodRanking instance.

Panics

Panics if the matrix is not square, if any diagonal element is nonzero, or if the pairwise sums a_ij + a_ji are not the same constant for all i != j.

pub fn matrix(&self) -> &Vec<Vec<i32>>

Returns the comparison matrix.

pub fn num_items(&self) -> usize

Returns the number of items to rank.

pub fn comparison_count(&self) -> i32

Returns the constant pairwise comparison count c.

Trait Implementations

impl Clone for MaximumLikelihoodRanking

fn clone(&self) -> MaximumLikelihoodRanking

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MaximumLikelihoodRanking

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for MaximumLikelihoodRanking

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Problem for MaximumLikelihoodRanking

const NAME: &'static str = "MaximumLikelihoodRanking"

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

type Value = Min<i64>

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]) -> Min<i64>

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<ILP> for MaximumLikelihoodRanking

type Result = ReductionMaximumLikelihoodRankingToILP

The reduction result type.

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

Reduce this problem to the target problem type.

impl ReduceTo<MaximumLikelihoodRanking> for MinimumFeedbackArcSet<i32>

type Result = ReductionFASToMLR

The reduction result type.

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

Reduce this problem to the target problem type.

impl Serialize for MaximumLikelihoodRanking

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 MaximumLikelihoodRanking