Zuma Game
Time O((b+h) * h!*(b+h-1)!/(b-1)!) · Space O((b+h) * h!*(b+h-1)!/(b-1)!) · Official statement on LeetCode
Solutions
// Time: O((b+h)^2 * h!*(b+h-1)!/(b-1)!)
// Space: O((b+h) * h!*(b+h-1)!/(b-1)!)
// brute force solution with worse complexity but pass
class Solution {
public:
int findMinStep(string board, string hand) {
unordered_map<string, unordered_map<string, int>> lookup;
int result = findMinStepHelper(board, hand, &lookup);
return result > hand.size() ? -1 : result;
}
private:
int findMinStepHelper(const string& board, const string& hand,
unordered_map<string, unordered_map<string, int>> *lookup) {
if (board.empty()) {
return 0;
}
if (hand.empty()) {
return MAX_STEP;
}
if ((*lookup)[board][hand]) {
return (*lookup)[board][hand];
}
int result = MAX_STEP;
for (int i = 0; i < hand.size(); ++i) {
for (int j = 0; j <= board.size(); ++j) {
const auto& next_board = shrink(board.substr(0, j) + hand.substr(i, 1) + board.substr(j));
const auto& next_hand = hand.substr(0, i) + hand.substr(i + 1);
result = min(result, findMinStepHelper(next_board, next_hand, lookup) + 1);
}
}
return (*lookup)[board][hand] = result;
}
string shrink(string s) { // Time: O(n^2), Space: O(1)
bool changed = true;
while (changed) {
changed = false;
for (int start = 0, i = 0; start < size(s); ++start) {
while(i < size(s) && s[start] == s[i]) {
++i;
}
if (i - start >= 3) {
s = s.substr(0, start) + s.substr(i);
changed = true;
break;
}
}
}
return s;
}
static const int MAX_STEP = 6;
};
// Time: O((b+h) * h!*(b+h-1)!/(b-1)!)
// Space: O((b+h) * h!*(b+h-1)!/(b-1)!)
// brute force solution
class Solution_TLE {
public:
int findMinStep(string board, string hand) {
unordered_map<string, unordered_map<string, int>> lookup;
int result = findMinStepHelper(board, hand, &lookup);
return result > hand.size() ? -1 : result;
}
private:
int findMinStepHelper(const string& board, const string& hand,
unordered_map<string, unordered_map<string, int>> *lookup) {
if (board.empty()) {
return 0;
}
if (hand.empty()) {
return MAX_STEP;
}
if ((*lookup)[board][hand]) {
return (*lookup)[board][hand];
}
int result = MAX_STEP;
for (int i = 0; i < hand.size(); ++i) {
for (int j = 0; j <= board.size(); ++j) {
const auto& next_board = shrink(board.substr(0, j) + hand.substr(i, 1) + board.substr(j));
const auto& next_hand = hand.substr(0, i) + hand.substr(i + 1);
result = min(result, findMinStepHelper(next_board, next_hand, lookup) + 1);
}
}
return (*lookup)[board][hand] = result;
}
string shrink(const string& s) { // Time: O(n), Space: O(n)
vector<pair<char, int>> stack;
for (int i = 0, start = 0; i <= s.size(); ++i) {
if (i == s.size() || s[i] != s[start]) {
if (!stack.empty() && stack.back().first == s[start]) {
stack.back().second += i - start;
if (stack.back().second >= 3) {
stack.pop_back();
}
} else if (!s.empty() && i - start < 3) {
stack.emplace_back(s[start], i - start);
}
start = i;
}
}
string result;
for (const auto& p : stack) {
result += string(p.second, p.first);
}
return result;
}
static const int MAX_STEP = 6;
};
// Time: O((b * h) * b * b! * h!)
// Space: O(b * b! * h!)
// greedy solution without proof (possibly incorrect)
class Solution_GREEDY_ACCEPT_BUT_NOT_PROVED {
public:
int findMinStep(string board, string hand) {
unordered_map<string, unordered_map<string, int>> lookup;
sort(hand.begin(), hand.end());
int result = findMinStepHelper(board, hand, &lookup);
if (result == MAX_STEP) {
unordered_map<string, unordered_map<string, int>> lookup2;
result = findMinStepHelper2(board, hand, &lookup2);
}
return result > hand.size() ? -1 : result;
}
private:
int findMinStepHelper(const string& board, const string& hand,
unordered_map<string, unordered_map<string, int>> *lookup) {
if (board.empty()) {
return 0;
}
if (hand.empty()) {
return MAX_STEP;
}
if ((*lookup)[board][hand]) {
return (*lookup)[board][hand];
}
int result = MAX_STEP;
for (int i = 0; i < hand.size(); ++i) {
int j = 0;
while (j < board.size()) {
int k = board.find(hand[i], j);
if (k == string::npos) {
break;
}
if (k < board.size() - 1 && board[k] == board[k + 1]) {
const auto& next_board = shrink(board.substr(0, k) + board.substr(k + 2));
const auto& next_hand = hand.substr(0, i) + hand.substr(i + 1);
result = min(result, findMinStepHelper(next_board, next_hand, lookup) + 1);
++k;
} else if (i > 0 && hand[i] == hand[i - 1]) {
const auto& next_board = shrink(board.substr(0, k) + board.substr(k + 1));
const auto& next_hand = hand.substr(0, i - 1) + hand.substr(i + 1);
result = min(result, findMinStepHelper(next_board, next_hand, lookup) + 2);
}
j = k + 1;
}
}
return (*lookup)[board][hand] = result;
}
int findMinStepHelper2(const string& board, const string& hand,
unordered_map<string, unordered_map<string, int>> *lookup) {
int result = MAX_STEP;
for (int i = 0; i < hand.size(); ++i) {
for (int j = 0; j <= board.size(); ++j) {
const auto& next_board = shrink(board.substr(0, j) + hand.substr(i, 1) + board.substr(j));
const auto& next_hand = hand.substr(0, i) + hand.substr(i + 1);
result = min(result, findMinStepHelper(next_board, next_hand, lookup) + 1);
}
}
return result;
}
string shrink(const string& s) { // Time: O(n), Space: O(n)
vector<pair<char, int>> stack;
for (int i = 0, start = 0; i <= s.size(); ++i) {
if (i == s.size() || s[i] != s[start]) {
if (!stack.empty() && stack.back().first == s[start]) {
stack.back().second += i - start;
if (stack.back().second >= 3) {
stack.pop_back();
}
} else if (!s.empty() && i - start < 3) {
stack.emplace_back(s[start], i - start);
}
start = i;
}
}
string result;
for (const auto& p : stack) {
result += string(p.second, p.first);
}
return result;
}
static const int MAX_STEP = 6;
};
// Time: O(b * b! * h!)
// Space: O(b * b! * h!)
// if a ball can be only inserted beside a ball with same color,
// we can do by this solution
class Solution_WRONG_GREEDY_AND_NOT_ACCEPT_NOW {
public:
int findMinStep(string board, string hand) {
unordered_map<string, unordered_map<string, int>> lookup;
sort(hand.begin(), hand.end());
int result = findMinStepHelper(board, hand, &lookup);
return result > hand.size() ? -1 : result;
}
private:
int findMinStepHelper(const string& board, const string& hand,
unordered_map<string, unordered_map<string, int>> *lookup) {
if (board.empty()) {
return 0;
}
if (hand.empty()) {
return MAX_STEP;
}
if ((*lookup)[board][hand]) {
return (*lookup)[board][hand];
}
int result = MAX_STEP;
for (int i = 0; i < hand.size(); ++i) {
int j = 0;
while (j < board.size()) {
int k = board.find(hand[i], j);
if (k == string::npos) {
break;
}
if (k < board.size() - 1 && board[k] == board[k + 1]) {
const auto& next_board = shrink(board.substr(0, k) + board.substr(k + 2));
const auto& next_hand = hand.substr(0, i) + hand.substr(i + 1);
result = min(result, findMinStepHelper(next_board, next_hand, lookup) + 1);
++k;
} else if (i > 0 && hand[i] == hand[i - 1]) {
const auto& next_board = shrink(board.substr(0, k) + board.substr(k + 1));
const auto& next_hand = hand.substr(0, i - 1) + hand.substr(i + 1);
result = min(result, findMinStepHelper(next_board, next_hand, lookup) + 2);
}
j = k + 1;
}
}
return (*lookup)[board][hand] = result;
}
string shrink(const string& s) { // Time: O(n), Space: O(n)
vector<pair<char, int>> stack;
for (int i = 0, start = 0; i <= s.size(); ++i) {
if (i == s.size() || s[i] != s[start]) {
if (!stack.empty() && stack.back().first == s[start]) {
stack.back().second += i - start;
if (stack.back().second >= 3) {
stack.pop_back();
}
} else if (!s.empty() && i - start < 3) {
stack.emplace_back(s[start], i - start);
}
start = i;
}
}
string result;
for (const auto& p : stack) {
result += string(p.second, p.first);
}
return result;
}
static const int MAX_STEP = 6;
};
Beginner Explanation
What is Zuma Game?
Zuma Game (LeetCode #488) is a Hard problem that primarily trains backtracking.
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 dfs backtracking.
- 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: Backtracking.
AlgoForge explanations are original teaching notes. Always open the official problem statement on LeetCode for constraints and examples.
Interview Walkthrough
Interview approach for Zuma Game
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 dfs backtracking.
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((b+h) * h!(b+h-1)!/(b-1)!)) and space (O((b+h) * h!(b+h-1)!/(b-1)!)) 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((b+h) * h!*(b+h-1)!/(b-1)!) time and O((b+h) * h!*(b+h-1)!/(b-1)!) space.
Pattern focus: dfs backtracking
Use the pattern as a checklist:
- dfs backtracking — 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((b+h) * h!*(b+h-1)!/(b-1)!) |
| Space | O((b+h) * h!*(b+h-1)!/(b-1)!) |
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 Zuma Game
- Skipping edge cases — empty collections, single-element inputs, max constraints.
- Wrong invariant for dfs backtracking — 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 dfs backtracking:
- 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: backtracking.
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 Zuma Game in a second language (cpp, python).
- Drill 3–5 more problems tagged backtracking.
- 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 dfs backtracking 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
Zuma Game (#488) — Hard. Pattern: dfs backtracking. Complexity: O((b+h) * h!*(b+h-1)!/(b-1)!) time / O((b+h) * h!*(b+h-1)!/(b-1)!) space. Re-derive the invariant before coding.
FAQs
What is the time complexity of Zuma Game?+
The reference solutions aim for O((b+h) * h!*(b+h-1)!/(b-1)!) time and O((b+h) * h!*(b+h-1)!/(b-1)!) space. Always re-derive complexity from the code you write in the interview.
What pattern does Zuma Game use?+
It primarily maps to dfs backtracking, within the broader topic of backtracking.
Is Zuma Game 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/zuma-game/