LeetCode #847 — HARD

Shortest Path Visiting All Nodes

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

The Problem

Problem Statement

You have an undirected, connected graph of n nodes labeled from 0 to n - 1. You are given an array graph where graph[i] is a list of all the nodes connected with node i by an edge.

Return the length of the shortest path that visits every node. You may start and stop at any node, you may revisit nodes multiple times, and you may reuse edges.

Example 1:

Input: graph = [[1,2,3],[0],[0],[0]]
Output: 4
Explanation: One possible path is [1,0,2,0,3]

Example 2:

Input: graph = [[1],[0,2,4],[1,3,4],[2],[1,2]]
Output: 4
Explanation: One possible path is [0,1,4,2,3]

Constraints:

n == graph.length
1 <= n <= 12
0 <= graph[i].length < n
graph[i] does not contain i.
If graph[a] contains b, then graph[b] contains a.
The input graph is always connected.

Step 01

Brute Force Baseline

Problem summary: You have an undirected, connected graph of n nodes labeled from 0 to n - 1. You are given an array graph where graph[i] is a list of all the nodes connected with node i by an edge. Return the length of the shortest path that visits every node. You may start and stop at any node, you may revisit nodes multiple times, and you may reuse edges.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Dynamic Programming · Bit Manipulation

Example 1

[[1,2,3],[0],[0],[0]]

Example 2

[[1],[0,2,4],[1,3,4],[2],[1,2]]

Core Insight

What unlocks the optimal approach

No official hints in dataset. Start from constraints and look for a monotonic or reusable state.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Largest constraint values

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #847: Shortest Path Visiting All Nodes
class Solution {
    public int shortestPathLength(int[][] graph) {
        int n = graph.length;
        Deque<int[]> q = new ArrayDeque<>();
        boolean[][] vis = new boolean[n][1 << n];
        for (int i = 0; i < n; ++i) {
            q.offer(new int[] {i, 1 << i});
            vis[i][1 << i] = true;
        }
        for (int ans = 0;; ++ans) {
            for (int k = q.size(); k > 0; --k) {
                var p = q.poll();
                int i = p[0], st = p[1];
                if (st == (1 << n) - 1) {
                    return ans;
                }
                for (int j : graph[i]) {
                    int nst = st | 1 << j;
                    if (!vis[j][nst]) {
                        vis[j][nst] = true;
                        q.offer(new int[] {j, nst});
                    }
                }
            }
        }
    }
}

// Accepted solution for LeetCode #847: Shortest Path Visiting All Nodes
func shortestPathLength(graph [][]int) int {
	n := len(graph)
	q := [][2]int{}
	vis := make([][]bool, n)
	for i := range vis {
		vis[i] = make([]bool, 1<<n)
		vis[i][1<<i] = true
		q = append(q, [2]int{i, 1 << i})
	}
	for ans := 0; ; ans++ {
		for k := len(q); k > 0; k-- {
			p := q[0]
			q = q[1:]
			i, st := p[0], p[1]
			if st == (1<<n)-1 {
				return ans
			}
			for _, j := range graph[i] {
				nst := st | 1<<j
				if !vis[j][nst] {
					vis[j][nst] = true
					q = append(q, [2]int{j, nst})
				}
			}
		}
	}
}

# Accepted solution for LeetCode #847: Shortest Path Visiting All Nodes
class Solution:
    def shortestPathLength(self, graph: List[List[int]]) -> int:
        n = len(graph)
        q = deque()
        vis = set()
        for i in range(n):
            q.append((i, 1 << i))
            vis.add((i, 1 << i))
        ans = 0
        while 1:
            for _ in range(len(q)):
                i, st = q.popleft()
                if st == (1 << n) - 1:
                    return ans
                for j in graph[i]:
                    nst = st | 1 << j
                    if (j, nst) not in vis:
                        vis.add((j, nst))
                        q.append((j, nst))
            ans += 1

// Accepted solution for LeetCode #847: Shortest Path Visiting All Nodes
use std::collections::VecDeque;

impl Solution {
    #[allow(dead_code)]
    pub fn shortest_path_length(graph: Vec<Vec<i32>>) -> i32 {
        let n = graph.len();
        let mut vis = vec![vec![false; 1 << n]; n];
        let mut q = VecDeque::new();

        // Initialize the queue
        for i in 0..n {
            q.push_back(((i, 1 << i), 0));
            vis[i][1 << i] = true;
        }

        // Begin BFS
        while !q.is_empty() {
            let ((i, st), count) = q.pop_front().unwrap();
            if st == (1 << n) - 1 {
                return count;
            }
            // If the path has not been visited
            for j in &graph[i] {
                let nst = st | (1 << *j);
                if !vis[*j as usize][nst] {
                    q.push_back(((*j as usize, nst), count + 1));
                    vis[*j as usize][nst] = true;
                }
            }
        }

        -1
    }
}

// Accepted solution for LeetCode #847: Shortest Path Visiting All Nodes
function shortestPathLength(graph: number[][]): number {
    const n = graph.length;
    const q: number[][] = [];
    const vis: boolean[][] = new Array(n).fill(false).map(() => new Array(1 << n).fill(false));
    for (let i = 0; i < n; ++i) {
        q.push([i, 1 << i]);
        vis[i][1 << i] = true;
    }
    for (let ans = 0; ; ++ans) {
        for (let k = q.length; k; --k) {
            const [i, st] = q.shift()!;
            if (st === (1 << n) - 1) {
                return ans;
            }
            for (const j of graph[i]) {
                const nst = st | (1 << j);
                if (!vis[j][nst]) {
                    vis[j][nst] = true;
                    q.push([j, nst]);
                }
            }
        }
    }
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n × m)

Space

O(n × m)

Approach Breakdown

RECURSIVE

O(2ⁿ) time

O(n) space

Pure recursion explores every possible choice at each step. With two choices per state (take or skip), the decision tree has 2ⁿ leaves. The recursion stack uses O(n) space. Many subproblems are recomputed exponentially many times.

DYNAMIC PROGRAMMING

O(n × m) time

O(n × m) space

Each cell in the DP table is computed exactly once from previously solved subproblems. The table dimensions determine both time and space. Look for the state variables — each unique combination of state values is one cell. Often a rolling array can reduce space by one dimension.

Shortcut: Count your DP state dimensions → that’s your time. Can you drop one? That’s your space optimization.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

State misses one required dimension

Wrong move: An incomplete state merges distinct subproblems and caches incorrect answers.

Usually fails on: Correctness breaks on cases that differ only in hidden state.

Fix: Define state so each unique subproblem maps to one DP cell.