Implement Magic Dictionary
Time O(n) · Space O(d) · Official statement on LeetCode
Solutions
// Time: O(n), n is the length of the word
// Space: O(d)
class MagicDictionary {
public:
/** Initialize your data structure here. */
MagicDictionary() {
}
/** Build a dictionary through a list of words */
void buildDict(vector<string> dict) {
string result;
for (const auto& s : dict) {
trie_.Insert(s);
}
}
/** Returns if there is any word in the trie that equals to the given word after modifying exactly one character */
bool search(string word) {
return find(word, &trie_, 0, true);
}
private:
struct TrieNode {
bool isString = false;
unordered_map<char, TrieNode *> leaves;
void Insert(const string& s) {
auto* p = this;
for (const auto& c : s) {
if (p->leaves.find(c) == p->leaves.cend()) {
p->leaves[c] = new TrieNode;
}
p = p->leaves[c];
}
p->isString = true;
}
~TrieNode() {
for (auto& kv : leaves) {
if (kv.second) {
delete kv.second;
}
}
}
};
bool find(const string& word, TrieNode *curr, int i, bool mistakeAllowed) {
if (i == word.length()) {
return curr->isString && !mistakeAllowed;
}
if (!curr->leaves.count(word[i])) {
return mistakeAllowed ?
any_of(curr->leaves.begin(), curr->leaves.end(),
[&](const pair<char, TrieNode *>& kvp) {
return find(word, kvp.second, i + 1, false);
}) :
false;
}
if (mistakeAllowed) {
return find(word, curr->leaves[word[i]], i + 1, true) ||
any_of(curr->leaves.begin(), curr->leaves.end(),
[&](const pair<char, TrieNode *>& kvp) {
return kvp.first != word[i] && find(word, kvp.second, i + 1, false);
});
}
return find(word, curr->leaves[word[i]], i + 1, false);
}
TrieNode trie_;
};
/**
* Your MagicDictionary object will be instantiated and called as such:
* MagicDictionary obj = new MagicDictionary();
* obj.buildDict(dict);
* bool param_2 = obj.search(word);
*/
Beginner Explanation
What is Implement Magic Dictionary?
Implement Magic Dictionary (LeetCode #676) is a Medium 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 trie and dfs backtracking.
- Only then translate the idea into code.
Why this problem matters
It sits in the sweet spot of interview difficulty: multiple valid approaches, clear trade-offs. Official solution notes mention: Trie, DFS.
AlgoForge explanations are original teaching notes. Always open the official problem statement on LeetCode for constraints and examples.
Interview Walkthrough
Interview approach for Implement Magic Dictionary
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 trie and 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(n)) and space (O(d)) 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(n) time and O(d) space.
Pattern focus: trie and dfs backtracking
Use the pattern as a checklist:
- trie — confirm the invariant holds after each step
- 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(n) |
| Space | O(d) |
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 Implement Magic Dictionary
- Skipping edge cases — empty collections, single-element inputs, max constraints.
- Wrong invariant for trie and 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 trie and 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 Implement Magic Dictionary 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 trie and 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
Implement Magic Dictionary (#676) — Medium. Pattern: trie and dfs backtracking. Complexity: O(n) time / O(d) space. Re-derive the invariant before coding.
FAQs
What is the time complexity of Implement Magic Dictionary?+
The reference solutions aim for O(n) time and O(d) space. Always re-derive complexity from the code you write in the interview.
What pattern does Implement Magic Dictionary use?+
It primarily maps to trie and dfs backtracking, within the broader topic of backtracking.
Is Implement Magic Dictionary good for interviews?+
Yes — as a Medium 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/implement-magic-dictionary/