Learn how to solve the Fractional Knapsack problem in Java using a greedy algorithm. Includes profit-per-weight ratio sorting, annotated code, sample output, and a step-by-step output explanation.
The Greedy Knapsack Problem/ Fractional Knapsack Problem is a classic example of a greedy algorithm. It differs from the 0/1 Knapsack Problem in that you are allowed to take fractions of an item rather than having to choose to take it or leave it entirely. In this blog post, we'll walk through a Java implementation of the greedy approach to solving the fractional knapsack problem, explain how the logic works, and break down the sample output. What is the Greedy Strategy for Knapsack? In the greedy method, we aim to maximize the total profit by picking items with the highest profit-to-weight ratio. Here's the approach: Calculate the profit-to-weight ratio for each item. Sort the items based on this ratio in descending order. Select items starting from the highest ratio until the knapsack is full: If the item fits, include it completely. If not, include as much of it as possible.
Sorting is a fundamental operation in computer science, and QuickSort is one of the most popular and efficient sorting algorithms. In this blog post, we’ll look at a Java implementation of QuickSort, explain how it works, break down its steps with a sample output, and even visualize its working. What is QuickSort? QuickSort is a highly efficient, comparison-based, divide-and-conquer sorting algorithm. It was developed by Tony Hoare in 1959 and is widely used due to its performance on average-case scenarios. How QuickSort Works: Divide: Select a pivot element from the array. Rearrange the array such that all elements less than the pivot go to its left, and all elements greater than the pivot go to its right. Conquer: Recursively apply the above step to the left and right subarrays. Combine: Since the array is sorted in-place, there’s no need for a combine step as in merge sort.
Quicksort is a divide and conquer algorithm. Quicksort first divides a large array into two smaller sub-arrays: the low elements and the high elements. Quicksort can then recursively sort the sub-arrays. The steps are: Pick an element, called a pivot, from the array.Reorder the array so that all elements with values less than the pivot come before the pivot, while all elements with values greater than the pivot come after it (equal values can go either way). After this partitioning, the pivot is in its final position. This is called the partition operation.Recursively apply the above steps to the sub-array of elements with smaller values and separately to the sub-array of elements with greater values. (Via Wikipedia)
Learn how Merge Sort works in Java with a complete divide-and-conquer implementation, descriptive variable names, annotated code, sample output, and a step-by-step output explanation.
Finding the minimum and maximum elements in a dataset is a common problem in computer science. While you can solve this using simple iteration, using a divide-and-conquer approach is more efficient in certain scenarios, especially in recursive algorithms. In this blog post, we’ll explore a recursive Java implementation of the Min-Max algorithm. What is the Min-Max Algorithm? The Min-Max algorithm splits the array into smaller parts, recursively determines the minimum and maximum of each part, and then combines the results. This divide-and-conquer approach reduces the number of comparisons compared to a naive linear method. Why Use Divide-and-Conquer? It minimizes the number of comparisons. More efficient for large datasets. Demonstrates recursion and good problem-solving strategies.
Searching for elements in large datasets is a common task in computer programming, and doing it efficiently can greatly improve performance. One such efficient method is binary search—an algorithm designed specifically for sorted data. In this blog post, we’ll walk through how binary search works conceptually and then explore a working Java program that performs binary search on a user-provided set of numbers. What is Binary Search? Binary search is a divide-and-conquer algorithm used to find the position of a target value within a sorted array. Rather than checking each element one by one like a linear search, binary search repeatedly divides the array into two halves and compares the target with the middle element. How Binary Search Works: Start with the middle element of the array. If the middle element is the target, return its index. If the target is smaller, repeat the search on the left half. If the target is greater, repeat the search on the right half. Continue this process until the element is found or the sub-array size becomes zero. Because it halves the number of elements to be checked each time, binary search has a time complexity of O(log n), making it much faster than linear search for large datasets.
Nobody can tell you if what you’re doing is good, meaningful or worthwhile. The more compelling the path, the more lonely it is. By Hugh Macleod via quotesondesign.com
Updated: The original APKStudio download link (CodePlex, 2014) is dead — CodePlex shut down in 2017. This post has been updated with current tools for decompiling and recompiling Android APK files. Decompiling an Android APK lets you inspect an app's resources, understand its structure, perform security audits, or modify open-source applications for personal use. Whether you're a developer reverse-engineering a library, a security researcher auditing an app's permissions, or just curious how an app works under the hood, there are several mature tools available in 2026 to get the job done. This guide walks you through each one so you can pick the right tool for your situation.
Note: This post covers the Symfony 2 directory structure (app/, src/, vendor/, web/ with app.php/app_dev.php front controllers), which changed fundamentally in Symfony 4 (2018). Modern Symfony uses public/ instead of web/, config/ instead of app/config/, and bin/console instead of app/console. See the current Symfony documentation. This post is preserved for historical reference. Symfony 2 is a PHP framework. When you download it, if noticed, Symfony 2 has a directory structure. By default there are 5 directories like app, bin, src,vendor, web. If you had developed any kind of application with Symfony 2 then you might have followed something like put your source code in src, put all static assets in web, write config files in app etc. Again this directory structure is configurable, that means you can customize it according to your project needs. Even though the directory names are self-explanatory, let's have a deeper look of this structure. By default Symfony consists of following directories, app/: The application configuration src/: The project’s PHP code vendor/: The third-party dependencies web/: The web root directory app/ 'app' directory in Symfony 2 holds the application configuration. You can find all configuration related stuff in 'app/config' folder. If you need to configure your database or Swiftmailer then you will need to change parameters/settings from here. Apart from that this directory holds the 'cache'. While writing code in Symfony 2 you will be working with lots of files ranging from xml, yml to php, twig, html etc. Controllers will be written in some file, routes will be defined in some another files, views will be written in some other file and so on. So while serving request Symfony has to read all these necessary files. Since the count of files is too high, to achieve great performance Symfony 2 has inbuilt caching. And this cached data will be stored in 'cache' directory. Again there is a 'log' directory which has debugging related data, especially useful in development.
