Computer science is the intellectual foundation beneath every line of code you write. While programming teaches you how to instruct a computer, computer science teaches you why certain approaches work better than others — why one algorithm processes a million records in seconds while another takes hours, why the right data structure makes the difference between elegant and unworkable code, and why the principles of object-oriented design produce software that survives contact with the real world.
MYCPLUS has been publishing computer science tutorials and practical programming guides since 2004, building a comprehensive resource covering the core theoretical and applied topics that every serious programmer needs to understand. This Computer Science hub brings together four interconnected areas of study — Algorithms, Data Structures, Object-Oriented Programming and Programming Styles — each with its own dedicated section of in-depth tutorials, practical examples and working source code.
Tis section gives you the theoretical understanding and hands-on programming experience to write better software, solve harder problems and think more clearly about how programs work. Whether you are studying for exams, preparing for technical interviews or simply becoming a stronger programmer — start here.
Explore the four core areas covered in this section:
→ Algorithms — sorting, searching, graph traversal and optimisation
→ Data Structures — arrays, linked lists, trees, stacks and graphs
→ Object-Oriented Programming — classes, inheritance and polymorphism
→ Programming Styles — procedural, functional and declarative paradigms
Why MYCPLUS: Trusted by programmers and computer science students since 2004 — MYCPLUS covers all four core computer science disciplines in one place, with practical implementations in C, C++ and Java designed for both academic study and real-world software development.
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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Delve into the fundamentals of data structures and algorithms with Data Structures and Algorithm Analysis in Java. This comprehensive guide focuses on performance optimization, scalability, and real-world Java implementations. From sorting to dynamic programming, it equips developers with the tools to build efficient, high-performance applications. A must-read for students and professionals seeking to master Java’s capabilities in solving complex computational problems.
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This document is a result of a committee formed at Indian Hill to establish a common set of coding standards and recommendations for the Indian Hill community. The scope of this work is the coding style, not the functional organization of programs. The standards in this document are not specific to ESS programming only.
Reference standards Don’t in any circumstances refer to Unix source code for or during your work on GNU! (Or to
This Java Program solves the Towers of Hanoi problem for a tower of 10 disks. Ten differently-sized disks are stacked in a pile, in order of decreasing size. There are two other places for piles. The object is to move the pile to the second available place, subject to the rules that only one disk at a time can be moved, and no disk can be piled on top of a smaller disk. The Towers of Hanoi problem is a standard example of recursion.
These algorithms are represented in terms of compact implementations in C so that readers can both understand their fundamental properties and implement them in real applications. The method of the analysis of algorithms is quite carefully designed. When appropriate, analytic results are described to explain why certain algorithms are preferred. In some cases, the practical algorithms’ relationship being discussed to purely theoretical results is also described.
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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.
Algorithms are at the core of computing. To be able to write an algorithm once and for all to work
