LeetCode 3653 | XOR After Range Multiplication Queries I | Java | Medium | Array | CodeByTushu
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In today’s video, we solve LeetCode 3653 — XOR After Range Multiplication Queries I using Array and Simulation in Java.
In this problem, we are given an array of integers nums and a list of queries. Each query specifies a starting index l, an ending index r, a step size k, and a multiplier v. For each query, we traverse indices from l to r stepping by k, multiplying each element by v modulo 10^9 + 7. After applying all queries sequentially, our goal is to return the bitwise XOR sum of all elements in the modified array.
Given the constraints where both the array length and the number of queries are at most 1000, simulating each query directly yields at most 10^6 total updates. We perform these updates in-place using 64-bit integer multiplication to avoid arithmetic overflow before applying the modulo operation. Finally, a single linear scan computes the cumulative XOR of all elements, achieving an optimal O(n + q * (n / k)) time complexity and O(1) auxiliary space.
🔥 Problem Highlights
✅ Direct Step Traversal: Stepping by k within range [l, r] for each query
✅ Overflow Prevention: Employing 64-bit long arithmetic prior to modulo 10^9 + 7
✅ In-Place Mutation: Modifying the original array directly without extra space overhead
✅ Bitwise XOR Reduction: Combining final element values via cumulative XOR aggregation
✅ Optimal Time Complexity: Executes at most 10^6 operations, finishing within 5ms
✅ Auxiliary Space Efficiency: Strictly O(1) auxiliary memory used
⏱️ TIMESTAMPS
00:00 Intro & Daily Challenge Kickoff (Streak 98 Days)
01:36 Problem Understanding & Range Query Mechanics
03:43 Key Insights: Bounded Complexity & 64-Bit Modulo
05:31 Optimal In-Place Simulation Algorithm
06:46 Clean Java Solution Walkthrough Line by Line
08:04 Test Cases Dry Run & Benchmark Verification (3 ms, Beats 99.4%)
09:52 Time Complexity Analysis (O(Q * (N/K) + N) Time)
10:54 Space Complexity Analysis (O(1) Auxiliary Space)
11:32 Day 8 Wrap-up & Channel Resources
📂 Problem Folder & Detailed Documentation:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/tree/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I
☕ Java Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I/Solution.java
🐍 Python Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I/solution.py
⚡ C++ Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I/solution.cpp
🌐 JavaScript Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I/solution.js
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💬 Question of the Day
Why is it essential to cast the array element and multiplier to a 64-bit integer before computing the modulo 10^9 + 7?
1️⃣ Java cannot compute modulo directly on 32-bit integers
2️⃣ Multiplying 10^9 by 10^5 can exceed 2^31 - 1, causing 32-bit signed integer overflow
3️⃣ Bitwise XOR only functions properly on 64-bit values
4️⃣ Modulo arithmetic with 10^9 + 7 automatically converts numbers to negative in 32-bit
#LeetCode3653 #LeetCode #XORAfterRangeMultiplicationQueriesI #Java #JavaDSA #Array #Simulation #BitManipulation #DSA #CodingInterview #ProblemSolving #LeetCodeMedium #DailyLeetCode #JavaProgramming #CodeByTushu
👇 Comment the answer below! 🔥 Close description
In today’s video, we solve LeetCode 3653 — XOR After Range Multiplication Queries I using Array and Simulation in Java.
In this problem, we are given an array of integers nums and a list of queries. Each query specifies a starting index l, an ending index r, a step size k, and a multiplier v. For each query, we traverse indices from l to r stepping by k, multiplying each element by v modulo 10^9 + 7. After applying all queries sequentially, our goal is to return the bitwise XOR sum of all elements in the modified array.
Given the constraints where both the array length and the number of queries are at most 1000, simulating each query directly yields at most 10^6 total updates. We perform these updates in-place using 64-bit integer multiplication to avoid arithmetic overflow before applying the modulo operation. Finally, a single linear scan computes the cumulative XOR of all elements, achieving an optimal O(n + q * (n / k)) time complexity and O(1) auxiliary space.
🔥 Problem Highlights
✅ Direct Step Traversal: Stepping by k within range [l, r] for each query
✅ Overflow Prevention: Employing 64-bit long arithmetic prior to modulo 10^9 + 7
✅ In-Place Mutation: Modifying the original array directly without extra space overhead
✅ Bitwise XOR Reduction: Combining final element values via cumulative XOR aggregation
✅ Optimal Time Complexity: Executes at most 10^6 operations, finishing within 5ms
✅ Auxiliary Space Efficiency: Strictly O(1) auxiliary memory used
⏱️ TIMESTAMPS
00:00 Intro & Daily Challenge Kickoff (Streak 98 Days)
01:36 Problem Understanding & Range Query Mechanics
03:43 Key Insights: Bounded Complexity & 64-Bit Modulo
05:31 Optimal In-Place Simulation Algorithm
06:46 Clean Java Solution Walkthrough Line by Line
08:04 Test Cases Dry Run & Benchmark Verification (3 ms, Beats 99.4%)
09:52 Time Complexity Analysis (O(Q * (N/K) + N) Time)
10:54 Space Complexity Analysis (O(1) Auxiliary Space)
11:32 Day 8 Wrap-up & Channel Resources
📂 Problem Folder & Detailed Documentation:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/tree/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I
☕ Java Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I/Solution.java
🐍 Python Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I/solution.py
⚡ C++ Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I/solution.cpp
🌐 JavaScript Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/April/08-April/3653-XOR-After-Range-Multiplication-Queries-I/solution.js
🔗 LeetCode Source Code
📌 Admin type the link
🌐 All LeetCode Solutions
📌 https://codebytushu.com/Leetcode/
📱 Join CodeByTushu on WhatsApp
Get coding updates, LeetCode solutions, daily questions and more.
https://whatsapp.com/channel/0029Vb8k5mCDeON2Xwlkxe40
❤️ Donate
📌 https://codebytushu.com/Leetcode/donate.php
🔥 Perfect For
LeetCode Medium Problems
Java DSA Preparation
Array & Simulation Patterns
Modular Arithmetic & Number Theory
Bit Manipulation Techniques
Time Complexity Optimization
Coding Interview Preparation
Problem Solving
🌐 Follow CodeByTushu
▶️ YouTube: https://www.youtube.com/@codebytushu
📸 Instagram: https://instagram.com/codebytushu/
💼 LinkedIn: https://www.linkedin.com/company/codebytushu/
🌍 Website: https://codebytushu.com
🐙 GitHub: https://github.com/CodeByTushu
❤️ Don’t Forget To
👍 Like | 💬 Comment | 🔔 Subscribe
💬 Question of the Day
Why is it essential to cast the array element and multiplier to a 64-bit integer before computing the modulo 10^9 + 7?
1️⃣ Java cannot compute modulo directly on 32-bit integers
2️⃣ Multiplying 10^9 by 10^5 can exceed 2^31 - 1, causing 32-bit signed integer overflow
3️⃣ Bitwise XOR only functions properly on 64-bit values
4️⃣ Modulo arithmetic with 10^9 + 7 automatically converts numbers to negative in 32-bit
#LeetCode3653 #LeetCode #XORAfterRangeMultiplicationQueriesI #Java #JavaDSA #Array #Simulation #BitManipulation #DSA #CodingInterview #ProblemSolving #LeetCodeMedium #DailyLeetCode #JavaProgramming #CodeByTushu
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