book

Learning Algorithms

by George Heineman

August 2021

Intermediate to advanced

280 pages

7h 10m

English

O'Reilly Media, Inc.

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Who This Book Is ForAbout the CodeConventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
What Is an Algorithm?Finding the Largest Value in an Arbitrary ListCounting Key OperationsModels Can Predict Algorithm PerformanceFind Two Largest Values in an Arbitrary ListTournament AlgorithmTime Complexity and Space ComplexitySummaryChallenge Exercises
Using Empirical Models to Predict PerformanceMultiplication Can Be FasterPerformance ClassesAsymptotic AnalysisCounting All OperationsCounting All BytesWhen One Door Closes, Another One OpensBinary Array SearchAlmost as Easy as πTwo Birds with One StonePulling It All TogetherCurve Fitting Versus Lower and Upper BoundsSummaryChallenge Exercises
Associating Values with KeysHash Functions and Hash CodesA Hashtable Structure for (Key, Value) PairsDetecting and Resolving Collisions with Linear ProbingSeparate Chaining with Linked ListsRemoving an Entry from a Linked ListEvaluationGrowing HashtablesAnalyzing the Performance of Dynamic HashtablesPerfect HashingIterate Over (key, value) PairsSummaryChallenge Exercises
Max Binary HeapsInserting a (value, priority)Removing the Value with Highest PriorityRepresenting a Binary Heap in an ArrayImplementation of Swim and SinkSummaryChallenge Exercises
Sorting by SwappingSelection SortAnatomy of a Quadratic Sorting AlgorithmAnalyze Performance of Insertion Sort and Selection SortRecursion and Divide and ConquerMerge SortQuicksortHeap SortPerformance Comparison of O(N log N) AlgorithmsTim SortSummaryChallenge Exercises
Getting StartedBinary Search TreesSearching for Values in a Binary Search TreeRemoving Values from a Binary Search TreeTraversing a Binary TreeAnalyzing Performance of Binary Search TreesSelf-Balancing Binary TreesAnalyzing Performance of Self-Balancing TreesUsing Binary Tree as (key, value) Symbol TableUsing the Binary Tree as a Priority QueueSummaryChallenge Exercises
Graphs Efficiently Store Useful InformationUsing Depth First Search to Solve a MazeBreadth First Search Offers Different Searching StrategyDirected GraphsGraphs with Edge WeightsDijkstra’s AlgorithmAll-Pairs Shortest PathFloyd–Warshall AlgorithmSummaryChallenge Exercises
Python Built-in Data TypesImplementing Stack in PythonImplementing Queues in PythonHeap and Priority Queue ImplementationsFuture Exploration

Content preview from Learning Algorithms

Foreword

Algorithms are at the heart of computer science and essential to the modern information age. They power the search engines used to answer billions of daily Internet search requests and provide privacy when communicating over the Internet. Algorithms are increasingly visible to consumers in countless areas, from customized advertising to online price quotes, and the news media is full of discussions about what algorithms are and what they can do.

The large growth in STEM (Science, Technology, Engineering and Mathematics) is powering a new wave of sustained growth and innovation in the global economy. But there simply aren’t enough computer scientists to discover and apply the algorithms needed for advances in medicine, engineering, and even government. We need to increase the number of people who know how to apply algorithms to the problems within their own fields and disciplines.

You don’t need a four-year degree in computer science to get started with algorithms. Unfortunately, most online material and textbooks on the topic are designed for undergraduate students, with an emphasis on mathematical proofs and computer science concepts. Algorithm textbooks can be intimidating because they are references for so many different algorithms, with countless variations and highly specialized cases. All too often, readers find it hard to complete the first chapter of these books. Using them can be a bit like trying to improve your English spelling by reading an entire dictionary: ...