What is the time complexity of Create Target Array in the Given Order?

The optimal solution has a time complexity of O(n^2).

What data structures are used to solve Create Target Array in the Given Order?

This problem uses Array, Simulation. Understanding these concepts is essential for solving this Easy problem efficiently.

What are the key insights for solving Create Target Array in the Given Order?

The problem highlights the importance of understanding array insertion operations and how they affect existing elements. Inserting an element into an array at a specific position requires shifting the subsequent elements to make space. Using a dynamic array (like `ArrayList` in Java or `vector` in C++) or a list data structure is crucial for efficient insertion at arbitrary indices because it handles resizing automatically. Using a fixed-size array would necessitate manual resizing and element shifting.

Which companies ask LeetCode 1389. Create Target Array in the Given Order in interviews?

This problem is frequently asked at Visa.

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Create Target Array in the Given Order - LeetCode 1389 Solution

Create Target Array in the Given Order - Complete Solution Guide

Create Target Array in the Given Order is LeetCode problem 1389, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

Given two arrays of integers nums and index . Your task is to create target array under the following rules: Initially target array is empty. From left to right read nums[i] and index[i], insert at index index[i] the value nums[i] in target array. Repeat the previous step until there are no elements to read in nums and index. Return the target array. It is guaranteed that the insertion operations will be valid. Example 1: Input: nums = [0,1,2,3,4], index = [0,1,2,2,1] Output: [0,4,1,3,2] Explana

Detailed Explanation

The problem asks you to create a target array from two input arrays: `nums` and `index`. `nums` contains the values to be inserted into the target array, and `index` specifies the positions where these values should be inserted. The insertion happens sequentially, starting from the first element of `nums` and `index`. The target array is initially empty. For each `i`, `nums[i]` is inserted into the target array at the index specified by `index[i]`. The problem guarantees that the `index` values are always valid (within the bounds of the current size of the target array during insertion).

Solution Approach

The provided solutions use a straightforward approach: they iterate through the `nums` and `index` arrays simultaneously. For each pair `(nums[i], index[i])`, they insert `nums[i]` into the target array at position `index[i]`. This is done using built-in functions such as `insert()` for `vector` (C++), `add()` for `ArrayList` (Java), and the `insert()` method for Python lists. The C solution manually handles the element shifting, reflecting the lower level control.

Step-by-Step Algorithm

Initialize an empty target array (or list).
Iterate through the `nums` and `index` arrays using a loop, from `i = 0` to `i = length(nums) - 1`.
In each iteration, insert `nums[i]` at the index `index[i]` in the target array. This may involve shifting existing elements if `index[i]` is not at the end of the array.
After the loop completes, the target array will contain all the elements from `nums` inserted at the positions specified by `index`.
Return the target array.

Key Insights

The problem highlights the importance of understanding array insertion operations and how they affect existing elements. Inserting an element into an array at a specific position requires shifting the subsequent elements to make space.
Using a dynamic array (like `ArrayList` in Java or `vector` in C++) or a list data structure is crucial for efficient insertion at arbitrary indices because it handles resizing automatically. Using a fixed-size array would necessitate manual resizing and element shifting.
The provided constraints (`0 <= index[i] <= i`) imply that we never need to insert at an index beyond the current end of the target array. This simplifies the insertion process somewhat.

Complexity Analysis

Time Complexity: O(n^2)

Space Complexity: O(n)

Topics

This problem involves: Array, Simulation.

Companies

Asked at: Visa.

Create Target Array in the Given Order | LeetCode 1389

Easy

Array Simulation

Time: O(n^2)Space: O(n)

Solution Code

Available in:

import java.util.ArrayList;
import java.util.List;

class Solution {
    public int[] createTargetArray(int[] nums, int[] index) {
        List<Integer> targetList = new ArrayList<>();
        for (int i = 0; i < nums.length; i++) {
            targetList.add(index[i], nums[i]);
        }
        int[] targetArray = new int[nums.length];
        for (int i = 0; i < nums.length; i++) {
            targetArray[i] = targetList.get(i);
        }
        return targetArray;
    }
}

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Problem Info

Description

Problem 1389: Create Target Array in the Given Order - Detailed explanation and solution approaches.

Topics

Array Simulation

Companies

Visa

Stats

Difficulty:Easy

Languages:4

Companies:1

Problem #:1389

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

5 steps

1
Initialize an empty target array (or list).
2
Iterate through the `nums` and `index` arrays using a loop, from `i = 0` to `i = length(nums) - 1`.
3
In each iteration, insert `nums[i]` at the index `index[i]` in the target array. This may involve shifting existing elements if `index[i]` is not at the end of the array.
4
After the loop completes, the target array will contain all the elements from `nums` inserted at the positions specified by `index`.
5
Return the target array.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n^2)

🐢 Quadratic - Gets slow with large inputs. Consider optimization!

💾

Space Complexity

O(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The time complexity is O(n^2) because the `insert` operation (in the Python, Java, and C++ solutions) at an arbitrary index in a dynamic array has a time complexity of O(n) in the worst case (insertion at the beginning of the array). Since we perform this operation `n` times (once for each element), the overall complexity becomes O(n^2). The C solution, while implementing insertion manually, still suffers from O(n) shifts for each insertion in the worst-case scenario. The space complexity is O(n) because we need to store the target array, which has a maximum size of `n`.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Using a temporary array

Instead of inserting directly into the target array, first create a temporary array of the same size as the input, where each element is initialized to a default value (like -1). Then, iterate through the `nums` and `index` arrays, and put the elements at their corresponding index positions in the temporary array. Finally, copy the values from the temporary array to the target array.

Time: O(n)Space: O(n)

Trade-offs: This approach achieves O(n) time complexity because the insertions are not shifting the elements. It is preferable to the initial approach when better time performance is a priority. The increased space complexity is usually negligible.

✨

Key Insights

1The problem highlights the importance of understanding array insertion operations and how they affect existing elements. Inserting an element into an array at a specific position requires shifting the subsequent elements to make space.
2Using a dynamic array (like `ArrayList` in Java or `vector` in C++) or a list data structure is crucial for efficient insertion at arbitrary indices because it handles resizing automatically. Using a fixed-size array would necessitate manual resizing and element shifting.
3The provided constraints (`0 <= index[i] <= i`) imply that we never need to insert at an index beyond the current end of the target array. This simplifies the insertion process somewhat.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Edge Cases

Empty input arrays: The solutions handle this gracefully by creating an empty target array; the loop simply doesn't execute.
All indices being at the end: If all `index[i] == i` the solution behaves like a simple copy, resulting in O(n) complexity because insertions happen at the end.

⚠️

Common Mistakes to Avoid

✗Forgetting to handle the shifting of elements when inserting at indices other than the end of the array. This would lead to incorrect results.

✗Using an approach with O(n^2) complexity when an O(n) approach exists, resulting in inefficient code for large input arrays.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Dynamically updating a ranked list: Imagine a leaderboard where new scores are inserted based on their rank.

🏦Managing a schedule of events: Inserting events into a calendar based on their start times.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Even for easy problems, discuss time and space complexity. Interviewers want to see you think analytically.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: October 8, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

In a real interview, always clarify constraints first. Ask about input size and value ranges.

@TechLeadJul 13

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 6

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJun 29

❓

Frequently Asked Questions

When should I use in-place modification vs creating a new array?

Use in-place modification when space complexity matters (O(1) space requirement) and the original array can be modified. Create a new array when you need to preserve the original data or when the problem involves significant restructuring that would complicate in-place logic.

What should I learn from easy problems?

Easy problems introduce core patterns that appear in harder problems. Master basic operations (iteration, conditionals, simple data structures), recognize common patterns (counting, searching, basic transformations), and practice explaining your thought process clearly.

What is the importance of time and space complexity analysis?

Complexity analysis is crucial because: 1) Interviewers always ask about it, 2) It helps you choose between approaches, 3) It demonstrates CS fundamentals. Always state both time and space complexity, and be prepared to explain how you derived them.

What is the optimal approach for "Create Target Array in the Given Order"?

For this easy-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether simulation techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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