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Home Java javaTutorial Searching vs. Sorting in Java: Key Differences and Applications

Searching vs. Sorting in Java: Key Differences and Applications

Jan 16, 2025 pm 12:28 PM

This article contrasts Java's searching and sorting algorithms, highlighting their distinct functions, methods, and time complexities. It provides practical examples and implementations, such as Merge Sort for data organization and Binary Search for efficient retrieval, showcasing their real-world problem-solving capabilities.

In Java, a firm grasp of searching and sorting algorithms, and their key differences, is essential for application functionality and effective data management. Searching pinpoints specific data within a dataset, while sorting rearranges the data itself. This article uses examples to explore their differences in purpose, methodology, and applications.

The core distinctions between Java's searching and sorting algorithms lie in their objectives, outputs, efficiency, and time complexities. Refer to Table 1 for a comparative analysis.

Table 1 Searching vs. Sorting in Java Searching vs. Sorting in Java: Key Differences and Applications

Algorithm selection often hinges on the desired outcome, application needs (dataset size, pre-sorted data, etc.), and specific requirements.

Table 2 illustrates pseudocode examples and time complexities for several search and sort algorithms:

Table 2 Runtime Complexities and Pseudocode Examples Searching vs. Sorting in Java: Key Differences and Applications Note: Without Java's Comparable interface, the code is only suitable for primitive data types. (Source: Lysecky, R., & Lizarraga, A. (2022). Programming in Java with ZyLabs, 18.3 O notation, Figure 18.3.2.)

Merge Sort, a divide-and-conquer algorithm, recursively splits a data array into smaller subarrays, sorts them, and then merges the sorted subarrays (GeeksforGeeks, 2020a). Binary Search, conversely, works on pre-sorted arrays, repeatedly halving the search interval until the target element is located or deemed absent (GeeksforGeeks, 2020b).

The following example demonstrates sorting an ArrayList of Book objects by publication year using Merge Sort, followed by a Binary Search on the sorted list:

Book.java

/**
 * Book object with title and publication year. Implements Comparable for year-based sorting.
 * 
 * @author Alexander Ricciardi
 * @version 1.0
 * @date 07/14/2024
 */
class Book implements Comparable<Book> {
    String title;
    int year;

    /**
     * Book constructor.
     * @param title Book title.
     * @param year Publication year.
     */
    public Book(String title, int year) {
        this.title = title;
        this.year = year;
    }

    /**
     * Compares books by publication year.
     * @param other Book to compare.
     * @return Comparison result.
     */
    @Override
    public int compareTo(Book other) {
        return Integer.compare(this.year, other.year);
    }

    /**
     * Returns book's string representation.
     * @return String representation.
     */
    @Override
    public String toString() {
        return title + " (" + year + ")";
    }
}

BookSortingSearching.java

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Scanner;

/**
 * Sorts and searches a list of books using merge sort and binary search.
 * 
 * @author Alexander Ricciardi
 * @version 1.0
 * @date 07/14/2024
 */
public class BookSortingSearching {

    // ... (mergeSort and binarySearch methods remain the same) ...

    public static void main(String[] args) {
        // ... (main method remains largely the same) ...
    }
}

...(The mergeSort and binarySearch methods would be included here, as they were in the original input. I've omitted them for brevity, as they are lengthy and already present.)

Output (example):

... (Original and sorted lists are displayed here) ...
<p>Enter a year to search for: 1951
Book found: The Catcher in the Rye (1951)</p>

Merge Sort's O(n log(n)) complexity makes it efficient for large datasets, while Binary Search's targeted approach is well-suited for applications like machine learning (e.g., finding optimal hyperparameters).

In conclusion, searching and sorting algorithms, while distinct, are interdependent. Sorting (like Merge Sort) prepares data for efficient searching (like Binary Search), making both indispensable for diverse problem-solving across various domains.


References:

GeeksforGeeks. (2020a, November 18). Merge sort. GeeksforGeeks. http://miracleart.cn/link/d0e7b521c18b09876cb7693e42880dba

GeeksforGeeks. (2020b, February 3). Binary search. GeeksforGeeks. http://miracleart.cn/link/d29af1fd577b037033dd1149e816d521

Lysecky, R., & Lizarraga, A. (2022). Programming in Java with ZyLabs. Zyante, Inc.


Originally published at Alex.omegapy on Medium by Level UP Coding on November 22, 2024.

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