What is the time complexity of Find the Sum of Encrypted Integers?

The optimal solution has a time complexity of O(n*m).

What data structures are used to solve Find the Sum of Encrypted Integers?

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

What are the key insights for solving Find the Sum of Encrypted Integers?

The encryption process is digit-based; we need to iterate through the digits of each number. Finding the maximum digit efficiently is crucial for performance.

Which companies ask LeetCode 3079. Find the Sum of Encrypted Integers in interviews?

This problem is frequently asked at Larsen & Toubro.

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Find the Sum of Encrypted Integers - LeetCode 3079 Solution

Find the Sum of Encrypted Integers - Complete Solution Guide

Find the Sum of Encrypted Integers is LeetCode problem 3079, 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

You are given an integer array nums containing positive integers. We define a function encrypt such that encrypt(x) replaces every digit in x with the largest digit in x . For example, encrypt(523) = 555 and encrypt(213) = 333 . Return the sum of encrypted elements . Example 1: Input: nums = [1,2,3] Output: 6 Explanation: The encrypted elements are [1,2,3] . The sum of encrypted elements is 1 + 2 + 3 == 6 . Example 2: Input: nums = [10,21,31] Output: 66 Explanation: The encrypted elements are [1

Detailed Explanation

The problem asks you to calculate the sum of encrypted integers. The encryption process involves taking each integer in the input array `nums`, finding the largest digit within that integer, and then replacing all digits in the integer with that largest digit. Finally, the sum of these encrypted integers is returned. For example, if the input is `[10, 21, 31]`, the largest digits are 1, 2, and 3 respectively. The encrypted numbers become `[11, 22, 33]`, and their sum is 66.

Solution Approach

The provided solutions use a similar approach. They iterate through each number in the input array. For each number, they find the largest digit. Then, they construct a new number by repeating the largest digit as many times as the number of digits in the original number. Finally, they sum up all the constructed encrypted numbers. The Python and Java solutions are more efficient and readable than the C++ and C solutions.

Step-by-Step Algorithm

Iterate through each number in the input array `nums`.
Convert the number to a string (or iterate through its digits using modulo operator).
Find the largest digit in the number.
Create a new string (or integer) by repeating the largest digit as many times as the original number has digits.
Convert the new string to an integer.
Add the encrypted integer to the running sum.
Return the final sum.

Key Insights

The encryption process is digit-based; we need to iterate through the digits of each number.
Finding the maximum digit efficiently is crucial for performance.
Converting numbers to strings and back can be a straightforward approach, though other methods using integer arithmetic are possible.

Complexity Analysis

Time Complexity: O(n*m)

Space Complexity: O(m)

Topics

This problem involves: Array, Math.

Companies

Asked at: Larsen & Toubro.

Find the Sum of Encrypted Integers | LeetCode 3079

Easy

Array Math

Time: O(n*m)Space: O(m)

Solution Code

Available in:

class Solution {
    public int sumOfEncryptedInt(int[] nums) {
        int sum = 0;
        for (int num : nums) {
            sum += encrypt(num);
        }
        return sum;
    }

    private int encrypt(int num) {
        String s = String.valueOf(num);
        int maxDigit = 0;
        for (char c : s.toCharArray()) {
            maxDigit = Math.max(maxDigit, c - '0');
        }
        int result = 0;
        for (int i = 0; i < s.length(); i++) {
            result = result * 10 + maxDigit;
        }
        return result;
    }
}

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Problem Info

Description

Problem 3079: Find the Sum of Encrypted Integers - Detailed explanation and solution approaches.

Topics

Array Math

Companies

Larsen & Toubro

Stats

Difficulty:Easy

Languages:4

Companies:1

Problem #:3079

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

7 steps

1
Iterate through each number in the input array `nums`.
2
Convert the number to a string (or iterate through its digits using modulo operator).
3
Find the largest digit in the number.
4
Create a new string (or integer) by repeating the largest digit as many times as the original number has digits.
5
Convert the new string to an integer.
6
Add the encrypted integer to the running sum.
7
Return the final sum.

💡

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*m)

💾

Space Complexity

O(m)

📝

Why?

The outer loop iterates `n` times. The inner loop (finding the max digit) iterates at most `m` times for each number. Therefore, the nested loop dominates the time complexity. The space used is proportional to the length of the longest number in the input array for storing the string representation. Constant space is used for variables, so it's negligible in comparison to O(m).

📖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

Integer Arithmetic

Instead of converting to strings, the largest digit can be found using modulo and integer division. The encrypted number can be built iteratively using powers of 10.

Time: O(n * m)Space: O(1)

Trade-offs: This approach avoids string manipulation, potentially offering slightly better performance in some cases, but it's less readable than the string-based approach. The space complexity is reduced to O(1), but the difference is negligible for practical input sizes.

✨

Key Insights

1The encryption process is digit-based; we need to iterate through the digits of each number.
2Finding the maximum digit efficiently is crucial for performance.
3Converting numbers to strings and back can be a straightforward approach, though other methods using integer arithmetic are possible.

📚

Key Terms Explained

For Students

Array

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

Edge Cases

Single-digit numbers: The solution correctly handles single-digit numbers; the encrypted number is the number itself.
Numbers with all the same digits: The solution works as expected in cases where all digits in a number are the same, for example, encrypt(555) = 555.
Zero: The C and C++ code needed special handling for 0. This is handled correctly in the final versions. The python and Java solutions inherently handle 0 correctly.

⚠️

Common Mistakes to Avoid

✗Incorrectly finding the largest digit: Failing to correctly iterate through the digits and compare them to find the true maximum.

✗Inefficient construction of the encrypted number: Using string concatenation repeatedly in the loop for building the encrypted number can lead to unnecessary overhead. Using mathematical approaches is more efficient.

✅

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?

💼Data anonymization: Replacing sensitive digits with a common value can help in protecting data privacy while still allowing for some analysis.

🏦Data transformation for specific algorithms: In some machine learning applications, it might be necessary to create a modified version of the data by replacing digits using a set rule.

💡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

Use this problem to demonstrate clean code practices. Variable naming and code organization matter.

⚠️ 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: June 20, 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 14

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

@DevExpert92Jul 7

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

@AlgoMasterJun 30

❓

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 "Find the Sum of Encrypted Integers"?

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 math techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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