Implementing Useful Algorithms in C 1st Edition by Dmytro Kedyk ISBN 8605325305 9798605325307

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ISBN 10: 8605325305 
ISBN 13: 9798605325307
Author: Dmytro Kedyk

Programmers use algorithms and data structures all the time, usually through numerous available APIs. Ideally an algorithm is correct, easy to understand, applicable to many problems, efficient, and free of intellectual property claims. This book covers algorithms and data structures learned in an algorithms class and many that aren’t, including statistical algorithms, external memory algorithms, numerical methods, optimization, string algorithms, and data compression.

About a fourth of the book is devoted to machine learning. There is much more theory than in the rest of the book because in machine learning relevant theory is more practical than algorithms. New learning algorithms are proposed often, and it’s easy to get lost without understanding how learning actually works. In particular, getting comfortable with the concept of estimation error substantially improves the ability to reason about statistical algorithms.

Another fourth is about numerical algorithms. Separate chapters cover matrix algorithms (such as eigenvalue calculation for spectral clustering), working with functions (integration, root finding, etc.), and optimization (both continuous and convex).

Expect to learn something new in every chapter. Many topics appear only in specialized books and papers, including collections of random number generators and hash functions for various use cases, priority queues that allow random access for applications like Djikstra’s shortest path algorithm, the simplex method for linear programming, efficient dictionaries for variable-length keys, Monte Carlo and bootstrap methods for statistical computing, top-performing learning algorithms such as random forest, etc. One of the goals of the book is answering all questions you might have had since taking an algorithms class.

Algorithm descriptions have tested C++ code, illustrations, performance analysis, and suggestions for optimizations and extensions. Still, the book is advanced, requiring some algorithmic maturity. After working through it, you will know more about algorithms and machine learning than before, even if you are already an expert. This is the book the author wishes he had when he started studying algorithms.

Implementing Useful Algorithms in C 1st Table of contents:

• Chapter 1: The C Programming Language

  • This introductory chapter covers the basics of the C language, including data types, operators, control flow, functions, pointers, and memory management, to ensure readers have the necessary foundation.

• Chapter 2: Data Structures

  • This chapter introduces fundamental data structures that are essential for implementing algorithms, such as linked lists, stacks, queues, and arrays.

• Chapter 3: Searching and Sorting Algorithms

  • Focuses on classical algorithms for finding and ordering data. This likely includes implementations of algorithms like Linear Search, Binary Search, Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, and Quick Sort.

• Chapter 4: Hash Functions

  • Covers the theory and implementation of hash functions, collision resolution strategies, and their use in data structures like hash tables.

• Chapter 5: Tree Data Structures

  • Explores tree-based data structures, including Binary Search Trees (BSTs), Balanced Trees (like AVL or Red-Black Trees), and their applications.

• Chapter 6: Graphs

  • Introduces graph theory concepts and algorithms for traversing graphs, such as Breadth-First Search (BFS) and Depth-First Search (DFS). It also likely covers algorithms for finding shortest paths, like Dijkstra’s algorithm.

• Chapter 7: Cryptographic Algorithms

  • This chapter delves into practical security algorithms, providing C implementations for encryption and decryption. This could include algorithms like AES (Advanced Encryption Standard) or RSA.

• Chapter 8: Data Compression Algorithms

  • Covers methods for reducing data size, with a focus on implementing algorithms like Huffman Coding or Lempel-Ziv (LZ) variations.

• Chapter 9: The Introduction to Machine Learning

  • This chapter provides a brief introduction to foundational machine learning concepts and may include a simple C implementation of a basic algorithm, such as linear regression or a simple neural network, to demonstrate the principles.

• Chapter 10: Let’s Use C++

  • This concluding chapter serves as a bridge, showing readers how the concepts and implementations learned in C can be adapted and improved upon using C++, highlighting key differences and advantages of the C++ language for algorithm development.

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Tags: Dmytro Kedyk, Implementing, Algorithms