Efficient Algorithm Design: Unlock the power of algorithms to optimize computer programming

Master advanced algorithm design techniques to tackle complex programming challenges and optimize application performance Key FeaturesDevelop advanced algorithm design skills to solve modern computational problems Learn state-of-the-art techniques to deepen your understanding of complex algorithms Apply your skills to real-world scenarios, enhancing your expertise in today's tech landscape Book DescriptionEfficient Algorithm Design redefines algorithms, tracing the evolution of computer science as a discipline bridging natural science and mathematics. Author Masoud Makrehchi, PhD, with his extensive experience in delivering publications and presentations, explores the duality of computers as mortal hardware and immortal algorithms. The book guides you through essential aspects of algorithm design and analysis, including proving correctness and the importance of repetition and loops. This groundwork sets the stage for exploring algorithm complexity, with practical exercises in design and analysis using sorting and search as examples. Each chapter delves into critical topics such as recursion and dynamic programming, reinforced with practical examples and exercises that link theory with real-world applications. What sets this book apart is its focus on the practical application of algorithm design and analysis, equipping you to solve real programming challenges effectively. By the end of this book, you’ll have a deep understanding of algorithmic foundations and gain proficiency in designing efficient algorithms, empowering you to develop more robust and optimized software solutions. What you will learnGain skills in advanced algorithm design for better problem-solving Understand algorithm correctness and complexity for robust software Apply theoretical concepts to real-world scenarios for practical solutions Master sorting and search algorithms, understanding their synergy Explore recursion and recurrence for complex algorithmic structures Leverage dynamic programming to optimize algorithms Grasp the impact of data structures on algorithm efficiency and design Who this book is forIf you’re a software engineer, computer scientist, or a student in a related field looking to deepen your understanding of algorithm design and analysis, this book is tailored for you. A foundation in programming and a grasp of basic mathematical concepts is recommended. It's an ideal resource for those already familiar with the basics of algorithms who want to explore more advanced topics. Data scientists and AI developers will find this book invaluable for enhancing their algorithmic approaches in practical applications. Efficient Algorithm Design Contributors About the author About the reviewer Preface Who this book is for What this book covers To get the most out of this book Download the example code files Conventions used Get in touch Share Your Thoughts Download a free PDF copy of this book Part 1: Foundations of Algorithm Analysis 1 Introduction to Algorithm Analysis Understanding algorithms and problem-solving The rationale for algorithm analysis The dual dimensions of algorithm analysis – efficiency and correctness Summary 2 Mathematical Induction and Loop Invariant for Algorithm Correctness Mathematical induction Loop invariants for the correctness of algorithms Summary References and further reading 3 Rate of Growth for Complexity Analysis Unpacking the rate of growth in algorithms Constant growth Sub-linear growth Linear growth Non-linear growth Asymptotic notations Simplification rules in asymptotic notation Asymptotic bounds Asymptotic upper bound (O notation) Asymptotic lower bound (-notation) Asymptotic tight bound (-notation) Confronting the unsolvable NP-hard problems Complexities of NP, NP-complete, and NP-hard Summary References and further reading 4 Recursion and Recurrence Functions Recursive algorithms The basics of recursion Types of recursion Recurrence functions Subtractive recurrence functions Divide-and-conquer recurrence functions Unfolding recurrence functions Summary References and further reading 5 Solving Recurrence Functions The substitution method Iteration approach or unrolling the recurrence Guessing and induction approach Variable change approach Recursion tree as a visualization technique The master theorem Case 1 – dominance of recursive calls or leaf-heavy recursion trees Case 2 – balanced growth or balanced recursion trees Case 3 – dominance of non-recursive work or root-heavy recursion trees Modified master theorem to solve subtracting recurrence functions Master theorem limitations Alternative approaches Beyond the master theorem – the Akra-Bazzi method Why does it work? Intuition behind Akra-Bazzi Summary References and further reading Part 2: Deep Dive in Algorithms 6 Sorting Algorithms The taxonomy of sorting algorithms Comparison Recursion Adaptability Inversion Memory usage Iterative sorting algorithms Bubble sort Selection sort Insertion sort Recursive sorting algorithms Merge sort Quick sort Non-comparison-based sorting Counting sort Radix sort Bucket sort Summary References and further reading 7 Search Algorithms Properties of search algorithms Linear-time and logarithmic search algorithms Linear or sequential search Sub-linear search Hashing Hash functions Constant time search using hashing Summary References and further reading 8 Symbiotic Relationship between Sort and Search Striking the right balance between sorting and searching Symbiotic link between sorting and searching The efficiency dilemma – to organize or not? Think like a computer scientist Summary References and further reading 9 Randomized Algorithms A review of probabilistic algorithms Non-deterministic algorithms Analysis of randomized algorithms Monty Hall problem Birthday paradox Hiring secretary problem Case studies Optimal selection in an online dating app Finding the closest parking spot Summary References and further reading 10 Dynamic Programming Dynamic programming versus divide-and-conquer Optimal substructure Overlapping subproblems Exploring dynamic programming Top-down versus bottom-up approaches for dynamic programming Solving the 0/1 knapsack problem using dynamic programming Limitations of dynamic programming Greedy algorithms – an introduction Traveling salesman problem Heuristics and their role in greedy algorithms Summary References and further reading Part 3: Fundamental Data Structures 11 Landscape of Data Structures Taxonomy of data structures Physical versus logical data structures Primitive versus composite data structures Linear versus non-linear data structures Static versus dynamic memory allocation Sequential versus random access Abstract data types Dictionaries Insertion Search Update Deletion Summary References and further reading 12 Linear Data Structures Lists Arrays Linked lists Skip lists Insertion in skip lists Search in skip lists Stacks Queue Deque Summary References and further reading 13 Non-Linear Data Structures Introduction to non-linear data structures Graphs Graphs representation Traversing graphs Trees Different types of trees and their properties Tree representation BSTs Heaps Heap operations Heapsort Summary References and further reading Part 4: Next Steps 14 Tomorrow’s Algorithms Learning from the past Scalability Context awareness Moral responsibility Moral consciousness in software practice Final words References and further reading Index Why subscribe? Other Books You May Enjoy Packt is searching for authors like you Share Your Thoughts Download a free PDF copy of this book Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary. Ey FeaturesLearn proven techniques from a heavyweight and recognized expert in C++ and high-performance computingUnderstand the limitations of modern CPUs and their performance impactFind out how you can avoid writing inefficient code and get the best optimizations from the compilerLearn the tradeoffs and costs of writing high-performance programsBook DescriptionThe great free lunch of "performance taking care of itself" is over. Until recently, programs got faster by themselves as CPUs were upgraded, but that doesn't happen anymore. The clock frequency of new processors has almost peaked, and while new architectures provide small improvements to existing programs, this only helps slightly. To write efficient software, you now have to know how to program by making good use of the available computing resources, and this book will teach you how to do that.The Art of Efficient Programming covers all the major aspects of writing efficient programs, such as using CPU resources and memory efficiently, avoiding unnecessary computations, measuring performance, and how to put concurrency and multithreading to good use. You'll also learn about compiler optimizations and how to use the programming language (C++) more efficiently. Finally, you'll understand how design decisions impact performance.By the end of this book, you'll not only have enough knowledge of processors and compilers to write efficient programs, but you'll also be able to understand which techniques to use and what to measure while improving performance. At its core, this book is about learning how to learn. What you will learnDiscover how to use the hardware computing resources in your programs effectivelyUnderstand the relationship between memory order and memory barriersFamiliarize yourself with the performance implications of different data structures and organizationsAssess the performance impact of concurrent memory accessed and how to minimize itDiscover when to use and when not to use lock-free programming techniquesExplore different ways to improve the effectiveness of compiler optimizationsDesign APIs for concurrent data structures and high-performance data structures to avoid inefficienciesWho this book is for This book is for experienced developers and programmers who work on performance-critical projects and want to learn new techniques to improve the performance of their code. Programmers in algorithmic trading, gaming, bioinformatics, computational genomics, or computational fluid dynamics communities will get the most out of the examples in this book, but the techniques are fairly universal. Although this book uses the C++ language, the concepts demonstrated in the book can be easily transferred or applied to other compiled languages such as C, Java, Rust, Go, and more Get to grips with various performance improvement techniques such as concurrency, lock-free programming, atomic operations, parallelism, and memory management Key Features Understand the limitations of modern CPUs and their performance impact Find out how you can avoid writing inefficient code and get the best optimizations from the compiler Learn the tradeoffs and costs of writing high-performance programs Book Description The great free lunch of "performance taking care of itself" is over. Until recently, programs got faster by themselves as CPUs were upgraded, but that doesn't happen anymore. The clock frequency of new processors has almost peaked. New architectures provide small improvements to existing programs, but this only helps slightly. Processors do get larger and more powerful, but most of this new power is consumed by the increased number of processing cores and other "extra" computing units. To write efficient software, you now have to know how to program by making good use of the available computing resources, and this book will teach you how to do that. The book covers all the major aspects of writing efficient programs, such as using CPU resources and memory efficiently, avoiding unnecessary computations, measuring performance, and how to put concurrency and multithreading to good use. You'll also learn about compiler optimizations and how to use the programming language (C++) more efficiently. Finally, you'll understand how design decisions impact performance. By the end of this book, you'll not only have enough knowledge of processors and compilers to write efficient programs, but you'll also be able to understand which techniques to use and what to measure while improving performance. At its core, this book is about learning how to learn. What you will learn Discover how to use the hardware computing resources in your programs effectively Understand the relationship between memory order and memory barriers Familiarize yourself with the performance implications of different data structures and organizations Assess the performance impact of concurrent memory accessed and how to minimize it Discover when to use and when not to use lock-free programming techniques Explore different ways to improve the effectiveness of compiler optimizations Design APIs for concurrent data structures and high-performance data structures to avoid inefficiencies Who this book is for This book is for experienced developers and programmers who work .. Solve classic computer science problems from fundamental algorithms, such as sorting and searching, to modern algorithms in machine learning and cryptography The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms. This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.Python programming experience is a must, knowledge of data science will be helpful but not necessary. Become a better programmer with performance improvement techniques such as concurrency, lock-free programming, atomic operations, parallelism, and memory management The great free lunch of "performance taking care of itself" is over. Until recently, programs got faster by themselves as CPUs were upgraded, but that doesn't happen anymore. The clock frequency of new processors has almost peaked, and while new architectures provide small improvements to existing programs, this only helps slightly. To write efficient software, you now have to know how to program by making good use of the available computing resources, and this book will teach you how to do that. The Art of Efficient Programming covers all the major aspects of writing efficient programs, such as using CPU resources and memory efficiently, avoiding unnecessary computations, measuring performance, and how to put concurrency and multithreading to good use. You'll also learn about compiler optimizations and how to use the programming language (C++) more efficiently. Finally, you'll understand how design decisions impact performance. By the end of this book, you'll not only have enough knowledge of processors and compilers to write efficient programs, but you'll also be able to understand which techniques to use and what to measure while improving performance. At its core, this book is about learning how to learn. This book is for experienced developers and programmers who work on performance-critical projects and want to learn new techniques to improve the performance of their code. Programmers in algorithmic trading, gaming, bioinformatics, computational genomics, or computational fluid dynamics communities will get the most out of the examples in this book, but the techniques are fairly universal. Although this book uses the C++ language, the concepts demonstrated in the book can be easily transferred or applied to other compiled languages such as C, Java, Rust, Go, and more. This book takes you through a systematic study of every aspect involved in improving the performance of your programs. It provides you with solutions to a range of performance-related troubleshooting scenarios and equips you with the knowledge and tools necessary for writing efficient programs while building performance-intensive applications.