You have to sort 1 GB of data with only 10... | আই সি টি Multiple Choice Question

<html lang="en"> <head> <meta charset="UTF-8"> <title>Sorting Technique Selection</title> <style> body { font-family: Arial, sans-serif; line-height: 1.6; } .content { max-width: 800px; margin: auto; padding: 20px; } .answer-choices { list-style-type: none; } .answer-choices li { margin: 8px 0; } .highlight { font-weight: bold; color: green; } </style> </head> <body> <div class="content"> <h2>Explanation for the Multiple Choice Question</h2>

Question: You have to sort 1 GB of data with only 100 MB of available main memory. Which sorting technique will be most appropriate?

Answer Choices:

Heap sort
Merge Sort
Insertion sort
Quick sort

The correct answer is <span class="highlight">Merge Sort</span>. Let's delve into the reasons why this is the most appropriate sorting technique given the constraints.

<h3>Why Merge Sort?</h3>

Merge Sort is a divide-and-conquer algorithm that is particularly effective for external sorting, where the data to be sorted cannot fit into memory.

Let's break down its significance given the constraints:

External Sorting: Merge Sort can handle large amounts of data by breaking it down into smaller chunks that can fit into the available memory. Since you have only 100 MB of memory available to sort 1 GB of data, Merge Sort efficiently divides the data into manageable segments.
Time Complexity: Merge Sort executes in $O(n \log n)$ time, which is quite efficient for sorting large datasets.
Stability: Merge Sort is a stable sort, meaning it preserves the order of equal elements, which can be important for certain applications.

<h3>Explanation of Other Choices:</h3>

Heap Sort: While Heap Sort has an $O(n \log n)$ time complexity and can be efficient in terms of memory usage, it is generally used for in-place sorts and may not be efficient for external sorting scenarios.

Insertion Sort: Insertion Sort performs well for small or nearly sorted datasets but has a time complexity of $O(n^2)$ in the average and worst cases. It is not suitable for sorting large volumes of data like 1 GB with only 100 MB of memory.

Quick Sort: Quick Sort has an average-case time complexity of $O(n \log n)$, but its worst-case complexity is $O(n^2)$. While it is a powerful algorithm for in-memory sorting, it is not as straightforward as Merge Sort for external sorting and may require additional data structures to handle the limited memory scenario.

Therefore, considering the need to handle data that cannot fit entirely into main memory (100 MB available for 1 GB of data), <span class="highlight">Merge Sort</span> is the most suitable algorithm due to its efficiency and suitability for external sorting.

</div> </body> </html>

You have to sort 1 GB of data with only 100 MB of available main memory. Which sorting technique will be most appropriate ?

বিস্তারিত ব্যাখ্যা

সকল অপশন

বিস্তারিত ব্যাখ্যা

সকল অপশন

সম্পর্কিত প্রশ্ন