book

Learning Algorithms

by George Heineman

August 2021

Intermediate to advanced

280 pages

7h 10m

English

O'Reilly Media, Inc.

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Includes Quizzes

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

Chapter 2. Analyzing Algorithms

This chapter introduces the terminology and notation used by theoreticians and practitioners alike in modeling the performance of algorithms in terms of computational performance and resource usage. When evaluating the runtime performance of your software program, you might be perfectly satisfied, in which case you can continue to use the application as is. But if you want to improve runtime performance, this book shows you where to start—with the program’s data structures and algorithms. You are faced with some specific questions:

Am I solving a specific problem in the most efficient way?: There may be other algorithms that would significantly improve performance.
Am I implementing an algorithm in the most efficient way?: There can be hidden performance costs that can be eliminated.
Should I just buy a faster computer?: The exact same program will have different runtime performance based ...