Concatenation of Array

In this problem, you are tasked with creating a new array where each element in the original array is repeated twice in sequence. The problem tests your understanding of array manipulation and efficient simulation of array concatenation.

Problem Statement

You are given an integer array nums of length n. Your task is to create an array ans of length 2n. The array ans must satisfy the condition that ans[i] == nums[i] and ans[i + n] == nums[i] for all valid indices 0 <= i < n.

In simpler terms, ans is formed by concatenating the nums array with itself. You need to return the resulting array ans.

Examples

Example 1

Input: nums = [1,2,1]

Output: [1,2,1,1,2,1]

The array ans is formed as follows:

• ans = [nums[0],nums[1],nums[2],nums[0],nums[1],nums[2]]
• ans = [1,2,1,1,2,1]

Example 2

Input: nums = [1,3,2,1]

Output: [1,3,2,1,1,3,2,1]

The array ans is formed as follows:

• ans = [nums[0],nums[1],nums[2],nums[3],nums[0],nums[1],nums[2],nums[3]]
• ans = [1,3,2,1,1,3,2,1]

Constraints

• n == nums.length
• 1 <= n <= 1000
• 1 <= nums[i] <= 1000

Solution Approach

Concatenation with Loop

One straightforward approach is to iterate over the nums array and append each element twice to the new ans array using a loop. This ensures that the final array is created with two repetitions of each element from nums.

Efficient Use of Python List Operations

Alternatively, you can leverage Python's list operations. By simply using list concatenation (nums + nums), the solution becomes very compact and avoids manual iteration.

In-place Simulation (If Required)

If memory usage is a concern, it may be possible to manipulate the original array and simulate the concatenation in-place. This, however, is more complex and less common in this particular problem setup.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

The time complexity is O(n) for both the loop-based and list concatenation solutions since we process each element once. Space complexity depends on the approach: the list concatenation approach uses O(n) space, while the in-place simulation may have reduced space usage if implemented properly.

What Interviewers Usually Probe

• The candidate should demonstrate an understanding of basic array manipulation.
• Look for familiarity with Python list operations, like concatenation or replication.
• Candidates may try to optimize space by simulating the process in-place.

Common Pitfalls or Variants

Common pitfalls

• Not considering edge cases such as very small arrays or arrays with repeating elements.
• Over-complicating the solution with unnecessary operations.
• Not optimizing space usage when memory constraints are part of the interview context.

Follow-up variants

• Create a solution with in-place operations (no additional space used).
• Modify the solution to handle larger inputs efficiently.
• Consider alternative programming languages and their array manipulation capabilities.

FAQ

What is the pattern in the Concatenation of Array problem?

The problem follows the 'Array plus Simulation' pattern, where you simulate array concatenation by manipulating indices.

How do I optimize space for the Concatenation of Array problem?

To optimize space, you can either modify the original array in-place or ensure that your array operations are as memory-efficient as possible.

Can I solve the Concatenation of Array problem without using extra space?

Yes, if the problem constraints allow for in-place manipulation of the original array, this can avoid using extra space.

What is the time complexity of the Concatenation of Array problem?

The time complexity is O(n), as you iterate through the array once to create the concatenated result.

Is there any edge case for the Concatenation of Array problem?

Yes, you should consider small arrays, such as when the array has only one element, or arrays with repetitive values.