C Programming Source Code

Code examples to demonstrate general programming concepts.

Solving the Knapsack Problem
C Programming Source Code

Solving the Knapsack Problem with Code Examples

The Knapsack Problem is a classic optimization problem in computer science and mathematics. The goal is to maximize the value of items placed in a knapsack without exceeding its weight capacity. This problem has many variations, but the most common are: 0/1 Knapsack Problem: Each item can either be included or excluded. Fractional Knapsack Problem: Items can be divided to maximize value.

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c-algorithms-library
C Programming Source Code

C Algorithms Library

This article is about a collection of common Computer Science algorithms which may be used in C projects. The C Programming Language has a much smaller Standard Library as compared to other more modern programming languages such as Java or Python. The library provides a basic set of mathematical functions, string manipulation, type conversions, and file and console-based I/O.

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Base 64 Encoding and Decoding
C Programming Source Code

C Implementation of Base64 Encoding and Decoding

Base64 encoding and decoding schemes are commonly used to encode binary data. Normally this is required when textual data needs to be transferred over the network or similar media and make sure that data is transferred without any modification. Base64 is commonly used in a number of applications, including email via MIME, and storing complex data in XML. This is a very simple implementation of base64 encoding and decoding in C programming language. There are number of C libraries available for encoding and decoding as well i.e. libb64, OpenSSL Base64, Apple’s Implementations, arduino-base64 etc.

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Kruskal's Algorithm
C Programming Source Code

Kruskal’s Algorithm

This is the implementation of Kruskal’s Algorithm in C and C++ Programming Languages. This algorithm is directly based on the generic MST (Minimum Spanning Tree) algorithm. Kruskal’s algorithm is a greedy algorithm in graph theory that finds a minimum spanning tree for a connected weighted graph. It finds a subset of the edges that forms a tree that includes every vertex, where the total weight of all the edges in the tree is minimized.

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