Minimum Moves to Move a Box to Their Target Location
Time O(m^2 * n^2) · Space O(m^2 * n^2) · Official statement on LeetCode
Solutions
// Time: O(m^2 * n^2)
// Space: O(m^2 * n^2)
// A* Search Algorithm without heap
class Solution {
public:
template <typename T>
struct PairHash {
size_t operator()(const pair<T, T>& p) const {
size_t seed = 0;
seed ^= std::hash<T>{}(p.first) + 0x9e3779b9 + (seed<<6) + (seed>>2);
seed ^= std::hash<T>{}(p.second) + 0x9e3779b9 + (seed<<6) + (seed>>2);
return seed;
}
};
int minPushBox(vector<vector<char>>& grid) {
pair<int, int> b, p, t;
for (int i = 0; i < grid.size(); ++i) {
for (int j = 0; j < grid[0].size(); ++j) {
if (grid[i][j] == 'B') {
b = {i, j};
} else if (grid[i][j] == 'S') {
p = {i, j};
} else if (grid[i][j] == 'T') {
t = {i, j};
}
}
}
return a_star(grid, b, p, t);
}
private:
int a_star(const vector<vector<char>>& grid,
const pair<int, int>& b,
const pair<int, int>& p,
const pair<int, int>& t) {
int f = g(b, t), dh = 2;
vector<pair<pair<int, int>, pair<int, int>>> closer{{b, p}}, detour;
unordered_set<pair<int, int>, PairHash<int>> lookup;
while (!closer.empty() || !detour.empty()) {
if (closer.empty()) {
f += dh;
swap(closer, detour);
}
const auto [b, p] = closer.back(); closer.pop_back();
if (b == t) {
return f;
}
if (lookup.count({b.first * grid[0].size() + b.second,
p.first * grid[0].size() + p.second})) {
continue;
}
lookup.emplace(b.first * grid[0].size() + b.second,
p.first * grid[0].size() + p.second);
for (const auto& [dx, dy] : directions) {
pair<int, int> nb = {b.first + dx, b.second + dy}, np = {b.first - dx, b.second - dy};
if (!(0 <= nb.first && nb.first < grid.size() &&
0 <= nb.second && nb.second < grid[0].size() &&
0 <= np.first && np.first < grid.size() &&
0 <= np.second && np.second < grid[0].size() &&
grid[nb.first][nb.second] != '#' && grid[np.first][np.second] != '#' &&
!lookup.count({nb.first * grid[0].size() + nb.second,
b.first * grid[0].size() + b.second}) &&
can_reach(grid, b, p, np))) {
continue;
}
if (dot({dx, dy}, {t.first - b.first, t.second - b.second}) > 0) {
closer.emplace_back(nb, b);
} else {
detour.emplace_back(nb, b);
}
}
}
return -1;
}
inline int g(const pair<int, int>& a,
const pair<int, int>& b) {
return abs(a.first - b.first) + abs(a.second - b.second);
}
int can_reach(const vector<vector<char>>& grid,
const pair<int, int>& b,
const pair<int, int>& p,
const pair<int, int>& t) {
vector<pair<int, int>> closer{p}, detour;
unordered_set<int> lookup = {b.first * grid[0].size() + b.second};
while (!closer.empty() || !detour.empty()) {
if (closer.empty()) {
swap(closer, detour);
}
auto p = closer.back(); closer.pop_back();
if (p == t) {
return true;
}
if (lookup.count(p.first * grid[0].size() + p.second)) {
continue;
}
lookup.emplace(p.first * grid[0].size() + p.second);
for (const auto& [dx, dy] : directions) {
pair<int, int> np = {p.first + dx, p.second + dy};
if (!(0 <= np.first && np.first < grid.size() &&
0 <= np.second && np.second < grid[0].size() &&
grid[np.first][np.second] != '#' &&
!lookup.count(np.first * grid[0].size() + np.second))) {
continue;
}
if (dot({dx, dy}, {t.first - p.first, t.second - p.second}) > 0) {
closer.emplace_back(np);
} else {
detour.emplace_back(np);
}
}
}
return false;
}
inline int dot(const pair<int, int>& a,
const pair<int, int>& b) {
return a.first * b.first + a.second * b.second;
}
static const vector<pair<int, int>> directions;
};
const vector<pair<int, int>> Solution::directions = {{0, 1}, {1, 0}, {0, -1}, {-1, 0}};
Beginner Explanation
What is Minimum Moves to Move a Box to Their Target Location?
Minimum Moves to Move a Box to Their Target Location (LeetCode #1263) is a Hard problem that primarily trains breadth first search.
How to think about it
- Restate the goal in your own words before coding.
- Work a tiny example by hand so the invariant becomes obvious.
- Identify the pattern — this problem aligns with a search algorithm.
- Only then translate the idea into code.
Why this problem matters
Hard problems force you to combine patterns and prove complexity carefully — interview gold. Official solution notes mention: A* Search Algorithm.
AlgoForge explanations are original teaching notes. Always open the official problem statement on LeetCode for constraints and examples.
Interview Walkthrough
Interview approach for Minimum Moves to Move a Box to Their Target Location
Opening (30–60 seconds)
- Clarify inputs/outputs and edge cases (empty input, single element, duplicates, overflow).
- State a brute force so the interviewer knows you can solve it naively.
- Propose the optimal direction tied to a search algorithm.
Core solution narrative
- Define the state you track (pointers, DP cell, set membership, stack top, etc.).
- Explain the transition when you process the next element.
- Call out time (O(m^2 * n^2)) and space (O(m^2 * n^2)) before coding.
- Code cleanly; narrate variable names.
What interviewers listen for
- Correctness on edge cases
- Complexity honesty
- Ability to discuss trade-offs (e.g., hash map space vs. sort + two pointers)
Follow-up questions they may ask
- Can you solve it with less memory?
- What if the input stream is infinite / doesn't fit in RAM?
- How would tests look for adversarial inputs?
Optimized Approach
Optimized solution notes
The reference solutions on AlgoForge target O(m^2 * n^2) time and O(m^2 * n^2) space.
Pattern focus: a search algorithm
Use the pattern as a checklist:
- a search algorithm — confirm the invariant holds after each step
Multiple methods appear in the source solutions — compare them and explain when each is preferable.
Implementation tips
- Prefer readable names over micro-optimizations in interviews.
- Extract helpers only when they clarify (e.g., expand-around-center, DFS visit).
- After AC-level logic, re-scan for off-by-one and null checks.
Complexity Analysis
Complexity
| Measure | Bound |
|---|---|
| Time | O(m^2 * n^2) |
| Space | O(m^2 * n^2) |
How to justify this in an interview
- Time: count loops, map/set operations, and recursive branching; state average vs worst case if relevant.
- Space: include hash maps, recursion stack, and output allocation when the problem asks for it.
If your implementation differs from the reference, re-derive big-O from your code — never memorize a complexity you cannot defend.
Common Mistakes
Common mistakes on Minimum Moves to Move a Box to Their Target Location
- Skipping edge cases — empty collections, single-element inputs, max constraints.
- Wrong invariant for a search algorithm — updating state too early or too late.
- Mutating input unexpectedly when the problem forbids it.
- Off-by-one in windows, ranges, or binary search bounds.
- Ignoring overflow / precision for integer arithmetic problems.
- Overengineering — jumping to an advanced structure when a simpler approach works.
Alternative Approaches
Alternatives
The source file includes more than one method. Compare:
- Primary optimized path — best complexity for typical interviews.
- Secondary approach — often brute force, sorting-based, or space-optimized variant.
Practice articulating when you would pick each (constraints, readability, follow-ups).
Edge Cases
Edge cases checklist
- Minimum input size
- Maximum input size / time limits
- Duplicates and already-sorted input
- Negative numbers / zeros (if applicable)
- Disconnected structures (graphs/trees)
- Single path vs branching recursion depth
Pattern Recognition
Spotting this pattern
Signal phrases that point to a search algorithm:
- Sorted input or ability to sort without changing the answer class
- Need for contiguous subarray / substring → consider sliding window
- Need for O(1) membership → hash set/map
- Optimal substructure + overlapping subproblems → DP
- Connectivity / components → graph DFS/BFS or Union-Find
Primary topics: breadth first search.
Follow-up Interview Questions
Follow-ups
- How does the solution change if the input is a stream?
- Can you solve it in-place?
- What if duplicates must be handled differently?
- How would you parallelize the approach?
- Design tests that would break a buggy implementation.
Practice Recommendations
What to practice next
- Re-solve Minimum Moves to Move a Box to Their Target Location in a second language (cpp, python).
- Drill 3–5 more problems tagged breadth first search.
- Teach the solution out loud in under 5 minutes.
- Add this problem to your revision calendar in 3 days and 14 days.
Visualization
Study checklist
- Read the official problem statement on LeetCode
- Solve on paper / whiteboard first
- Implement the a search algorithm approach
- Verify edge cases from the checklist
- State time and space complexity aloud
- Compare with the AlgoForge reference solution
- Schedule a revision session
Revision notes
Minimum Moves to Move a Box to Their Target Location (#1263) — Hard. Pattern: a search algorithm. Complexity: O(m^2 * n^2) time / O(m^2 * n^2) space. Re-derive the invariant before coding.
FAQs
What is the time complexity of Minimum Moves to Move a Box to Their Target Location?+
The reference solutions aim for O(m^2 * n^2) time and O(m^2 * n^2) space. Always re-derive complexity from the code you write in the interview.
What pattern does Minimum Moves to Move a Box to Their Target Location use?+
It primarily maps to a search algorithm, within the broader topic of breadth first search.
Is Minimum Moves to Move a Box to Their Target Location good for interviews?+
Yes — as a Hard problem it is a solid practice target. Pair it with related problems in the same pattern family for spaced repetition.
Where can I read the official statement?+
Open the official LeetCode page for constraints and examples: https://leetcode.com/problems/minimum-moves-to-move-a-box-to-their-target-location/