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feat: Add Hungarian Algorithm (Assignment Problem) with tests and reference [GRAPHS] (#6808)
* feat(graph): add Hungarian Algorithm (assignment) with tests; update directory * style(graph): split multiple variable declarations to satisfy Checkstyle
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src/main/java/com/thealgorithms/graph/HungarianAlgorithm.java
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src/main/java/com/thealgorithms/graph/HungarianAlgorithm.java
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package com.thealgorithms.graph;
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import java.util.Arrays;
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/**
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* Hungarian algorithm (a.k.a. Kuhn–Munkres) for the Assignment Problem.
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*
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* <p>Given an n x m cost matrix (n tasks, m workers), finds a minimum-cost
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* one-to-one assignment. If the matrix is rectangular, the algorithm pads to a
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* square internally. Costs must be finite non-negative integers.
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*
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* <p>Time complexity: O(n^3) with n = max(rows, cols).
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*
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* <p>API returns the assignment as an array where {@code assignment[i]} is the
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* column chosen for row i (or -1 if unassigned when rows != cols), and a total
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* minimal cost.
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*
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* @see <a href="https://en.wikipedia.org/wiki/Hungarian_algorithm">Wikipedia: Hungarian algorithm</a>
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*/
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public final class HungarianAlgorithm {
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private HungarianAlgorithm() {
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}
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/** Result holder for the Hungarian algorithm. */
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public static final class Result {
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public final int[] assignment; // assignment[row] = col or -1
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public final int minCost;
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public Result(int[] assignment, int minCost) {
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this.assignment = assignment;
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this.minCost = minCost;
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}
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}
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/**
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* Solves the assignment problem for a non-negative cost matrix.
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*
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* @param cost an r x c matrix of non-negative costs
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* @return Result with row-to-column assignment and minimal total cost
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* @throws IllegalArgumentException for null/empty or negative costs
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*/
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public static Result solve(int[][] cost) {
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validate(cost);
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int rows = cost.length;
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int cols = cost[0].length;
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int n = Math.max(rows, cols);
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// Build square matrix with padding 0 for missing cells
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int[][] a = new int[n][n];
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for (int i = 0; i < n; i++) {
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if (i < rows) {
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for (int j = 0; j < n; j++) {
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a[i][j] = (j < cols) ? cost[i][j] : 0;
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}
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} else {
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Arrays.fill(a[i], 0);
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}
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}
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// Potentials and matching arrays
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int[] u = new int[n + 1];
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int[] v = new int[n + 1];
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int[] p = new int[n + 1];
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int[] way = new int[n + 1];
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for (int i = 1; i <= n; i++) {
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p[0] = i;
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int j0 = 0;
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int[] minv = new int[n + 1];
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boolean[] used = new boolean[n + 1];
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Arrays.fill(minv, Integer.MAX_VALUE);
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Arrays.fill(used, false);
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do {
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used[j0] = true;
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int i0 = p[j0];
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int delta = Integer.MAX_VALUE;
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int j1 = 0;
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for (int j = 1; j <= n; j++) {
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if (!used[j]) {
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int cur = a[i0 - 1][j - 1] - u[i0] - v[j];
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if (cur < minv[j]) {
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minv[j] = cur;
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way[j] = j0;
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}
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if (minv[j] < delta) {
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delta = minv[j];
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j1 = j;
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}
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}
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}
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for (int j = 0; j <= n; j++) {
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if (used[j]) {
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u[p[j]] += delta;
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v[j] -= delta;
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} else {
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minv[j] -= delta;
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}
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}
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j0 = j1;
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} while (p[j0] != 0);
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do {
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int j1 = way[j0];
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p[j0] = p[j1];
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j0 = j1;
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} while (j0 != 0);
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}
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int[] matchColForRow = new int[n];
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Arrays.fill(matchColForRow, -1);
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for (int j = 1; j <= n; j++) {
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if (p[j] != 0) {
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matchColForRow[p[j] - 1] = j - 1;
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}
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}
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// Build assignment for original rows only, ignore padded rows
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int[] assignment = new int[rows];
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Arrays.fill(assignment, -1);
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int total = 0;
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for (int i = 0; i < rows; i++) {
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int j = matchColForRow[i];
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if (j >= 0 && j < cols) {
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assignment[i] = j;
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total += cost[i][j];
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}
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}
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return new Result(assignment, total);
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}
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private static void validate(int[][] cost) {
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if (cost == null || cost.length == 0) {
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throw new IllegalArgumentException("Cost matrix must not be null or empty");
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}
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int c = cost[0].length;
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if (c == 0) {
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throw new IllegalArgumentException("Cost matrix must have at least 1 column");
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}
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for (int i = 0; i < cost.length; i++) {
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if (cost[i] == null || cost[i].length != c) {
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throw new IllegalArgumentException("Cost matrix must be rectangular with equal row lengths");
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}
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for (int j = 0; j < c; j++) {
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if (cost[i][j] < 0) {
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throw new IllegalArgumentException("Costs must be non-negative");
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}
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}
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}
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}
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}
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