Serialization is the process of converting a data structure or object into a sequence of bits so that it can be stored in a file or memory buffer, or transmitted across a network connection link to be reconstructed later in the same or another computer environment.
Design an algorithm to serialize and deserialize a binary tree. There is no restriction on how your serialization/deserialization algorithm should work. You just need to ensure that a binary tree can be serialized to a string and this string can be deserialized to the original tree structure.
Clarification: The input/output format is the same as how LeetCode serializes a binary tree. You do not necessarily need to follow this format, so please be creative and come up with different approaches yourself.
Example 1:
Input: root = [1,2,3,null,null,4,5] Output: [1,2,3,null,null,4,5]
Example 2:
Input: root = [] Output: []
Constraints:
[0, 104].-1000 <= Node.val <= 1000This approach involves using a HashMap (or dictionary in Python) to count the occurrences of each element in the array. This will help in efficiently checking if an element exists and how many times it appears, which is useful for problems requiring frequency analysis.
This C solution uses an array to simulate a hash table where each index represents an element and its value represents the frequency. This allows both insertion and lookup to occur in constant O(1) time.
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Time Complexity: O(n), as it iterates over the array once. Space Complexity: O(1) if the element range is known and constant, or O(k) where k is the range of elements.
Another approach is sorting the array and then identifying repeated elements by comparison with neighboring elements. This leverages the efficiency of modern sorting algorithms to bring repeated elements together, making it trivial to count consecutive duplicates.
This C solution sorts the input array using quicksort and then counts consecutive elements to tally occurrences. Sorting ensures similar elements cluster together, thus simplifying frequency determination.
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Time Complexity: O(n log n) due to the sort operation. Space Complexity: O(1) if in-place sorting is considered.
| Approach | Complexity |
|---|---|
| Using HashMap for Counting Elements | Time Complexity: O(n), as it iterates over the array once. Space Complexity: O(1) if the element range is known and constant, or O(k) where k is the range of elements. |
| Using Sorting to Identify Repeated Elements | Time Complexity: O(n log n) due to the sort operation. Space Complexity: O(1) if in-place sorting is considered. |
L36. Serialize and De-serialize Binary Tree | C++ | Java • take U forward • 175,648 views views
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