LeetCode Problem Workspace
Flatten Deeply Nested Array
Flatten Deeply Nested Array involves transforming multi-dimensional arrays based on depth constraints, requiring a careful balance of recursion and iteration.
0
Topics
1
Code langs
0
Related
Practice Focus
Medium · Flatten Deeply Nested Array core interview pattern
Answer-first summary
Flatten Deeply Nested Array involves transforming multi-dimensional arrays based on depth constraints, requiring a careful balance of recursion and iteration.
Ace coding interviews with Interview AiBoxInterview AiBox guidance for Flatten Deeply Nested Array core interview pattern
The problem requires flattening a deeply nested array based on a given depth, using recursion to manage subarray flattening. The solution relies on tracking the depth of recursion to determine when to flatten each level. At depth n, subarrays at or below that depth are flattened accordingly, preserving higher-level nesting where necessary.
Problem Statement
Given a multi-dimensional array arr and an integer depth n, return a flattened version of the array. A multi-dimensional array is one that can contain integers or other nested arrays. The flattening process should only occur when the depth of a nested array is less than n.
Each element in the array is either an integer or another array. At depth n, arrays that are nested deeper than n should remain unflattened, while arrays at or below that depth should be completely flattened. The flattening operation should respect the provided depth limit.
Examples
Example 1
Input: See original problem statement.
Output: See original problem statement.
Input arr = [1, 2, 3, [4, 5, 6], [7, 8, [9, 10, 11], 12], [13, 14, 15]] n = 0 Output [1, 2, 3, [4, 5, 6], [7, 8, [9, 10, 11], 12], [13, 14, 15]]
Explanation Passing a depth of n=0 will always result in the original array. This is because the smallest possible depth of a subarray (0) is not less than n=0. Thus, no subarray should be flattened.
Example 2
Input: See original problem statement.
Output: See original problem statement.
Input arr = [1, 2, 3, [4, 5, 6], [7, 8, [9, 10, 11], 12], [13, 14, 15]] n = 1 Output [1, 2, 3, 4, 5, 6, 7, 8, [9, 10, 11], 12, 13, 14, 15]
Explanation The subarrays starting with 4, 7, and 13 are all flattened. This is because their depth of 0 is less than 1. However [9, 10, 11] remains unflattened because its depth is 1.
Example 3
Input: See original problem statement.
Output: See original problem statement.
Input arr = [[1, 2, 3], [4, 5, 6], [7, 8, [9, 10, 11], 12], [13, 14, 15]] n = 2 Output [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
Explanation The maximum depth of any subarray is 1. Thus, all of them are flattened.
Constraints
- 0 <= count of numbers in arr <= 105
- 0 <= count of subarrays in arr <= 105
- maxDepth <= 1000
- -1000 <= each number <= 1000
- 0 <= n <= 1000
Solution Approach
Recursive Flattening
A recursive approach can be implemented to iterate through the array. At each level, the recursion checks if the depth is less than n, and if so, flattens the array. This method ensures that elements are only flattened based on their depth, stopping further flattening once the depth limit is reached.
Iterative Depth Tracking
An iterative solution may use a stack or queue to manage array elements and their depths. By iterating over the array, each element is processed based on its current depth, allowing for control over when to flatten. The depth of each sub-array is checked and updated accordingly, following the same flattening rule.
Hybrid Recursive and Iterative Methods
A combination of recursion and iteration may optimize for performance in scenarios with deeper nesting. The recursion handles direct flattening of nested arrays, while iteration controls depth management, ensuring the solution remains efficient even for larger or more complex nested structures.
Complexity Analysis
| Metric | Value |
|---|---|
| Time | Depends on the final approach |
| Space | Depends on the final approach |
The time and space complexity of this solution depend on the depth and size of the input array. A recursive approach may lead to a time complexity of O(n), where n is the total number of elements, but the space complexity can be O(d) where d is the maximum recursion depth. Iterative approaches may offer better space efficiency by avoiding the overhead of recursion but still depend on array size.
What Interviewers Usually Probe
- Look for candidates who can implement recursion or iteration effectively to manage the depth of nested arrays.
- Candidates should demonstrate an understanding of managing both space and time complexity when flattening arrays.
- Watch for efficient solutions that avoid unnecessary recomputation or deep recursion, particularly for large arrays.
Common Pitfalls or Variants
Common pitfalls
- Overusing recursion can lead to excessive stack usage, especially when dealing with large arrays and deep nesting.
- Failing to properly track and limit depth can result in flattening elements that should remain nested, which breaks the problem constraints.
- Inefficient iteration methods may lead to excessive complexity, especially if they fail to optimize depth handling or flattening procedures.
Follow-up variants
- Flatten the array only up to a certain depth, leaving subarrays deeper than the specified depth intact.
- Ensure that the flattened version maintains the relative order of elements in all sub-arrays.
- Support additional custom depth rules, where different arrays may have different flattening limits.
FAQ
What is the 'Flatten Deeply Nested Array' problem?
The problem involves flattening a multi-dimensional array up to a specified depth, ensuring elements are processed only within that depth limit.
How can recursion be used to solve the problem?
Recursion can be used to navigate through nested arrays, flattening elements until the specified depth is reached.
What are common mistakes in solving this problem?
Common mistakes include not correctly managing recursion depth or failing to maintain the required structure of the original array.
How does the problem's complexity affect performance?
The complexity depends on both the size of the array and the depth of nesting. Inefficient depth management can lead to high time and space complexity.
What interview pattern is this problem based on?
This problem is a classic example of the 'Flatten Deeply Nested Array' core interview pattern, testing recursion and array manipulation skills.
Solution
Solution 1
#### TypeScript
type MultiDimensionalArray = (number | MultiDimensionalArray)[];
var flat = function (arr: MultiDimensionalArray, n: number): MultiDimensionalArray {
if (!n) {
return arr;
}
const ans: MultiDimensionalArray = [];
for (const x of arr) {
if (Array.isArray(x) && n) {
ans.push(...flat(x, n - 1));
} else {
ans.push(x);
}
}
return ans;
};