Algorithms For Dummies

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Algorithms For Dummies2ed(2022)[Mueller Massaron][9781119869986].pdf Title Page Copyright Page Table of Contents Introduction About This Book Foolish Assumptions Icons Used in This Book Beyond the Book Where to Go from Here Part 1 Getting Started with Algorithms Chapter 1 Introducing Algorithms Describing Algorithms The right way to make toast: Defining algorithm uses Finding algorithms everywhere Using Computers to Solve Problems Getting the most out of modern CPUs and GPUs Working with special-purpose chips Networks: Sharing is more than caring Leveraging available data Distinguishing between Issues and Solutions Being correct and efficient Discovering there is no free lunch Adapting the strategy to the problem Describing algorithms in a lingua franca Facing problems that are like brick walls, only harder Structuring Data to Obtain a Solution Understanding a computer’s point of view Arranging data makes the difference Chapter 2 Considering Algorithm Design Starting to Solve a Problem Modeling real-world problems Finding solutions and counterexamples Standing on the shoulders of giants Dividing and Conquering Avoiding brute-force solutions Keeping it simple, silly (KISS) Breaking down a problem is usually better Learning that Greed Can Be Good Applying greedy reasoning Reaching a good solution Computing Costs and Following Heuristics Representing the problem as a space Going random and being blessed by luck Using a heuristic and a cost function Evaluating Algorithms Simulating using abstract machines Getting even more abstract Working with functions Chapter 3 Working with Google Colab Defining Google Colab Understanding what Google Colab does Getting familiar with Google Colab features Working with Notebooks Creating a new notebook Opening existing notebooks Saving notebooks Performing Common Tasks Creating code cells Creating text cells Creating special cells Editing cells Moving cells Using Hardware Acceleration Executing the Code Getting Help Chapter 4 Performing Essential Data Manipulations Using Python Performing Calculations Using Vectors and Matrixes Understanding scalar and vector operations Performing vector multiplication Creating a matrix is the right way to start Multiplying matrixes Defining advanced matrix operations Creating Combinations the Right Way Distinguishing permutations Shuffling combinations Facing repetitions Getting the Desired Results Using Recursion Explaining recursion Eliminating tail call recursion Performing Tasks More Quickly Considering divide and conquer Distinguishing between different possible solutions Chapter 5 Developing a Matrix Computation Class Avoiding the Use of NumPy Understanding Why Using a Class is Important Building the Basic Class Creating a matrix Printing the resulting matrix Accessing specific matrix elements Performing scalar and matrix addition Performing multiplication Manipulating the Matrix Transposing a matrix Calculating the determinant Flattening the matrix Part 2 Understanding the Need to Sort and Search Chapter 6 Structuring Data Determining the Need for Structure Making it easier to see the content Matching data from various sources Considering the need for remediation Stacking and Piling Data in Order Ordering in stacks Using queues Finding data using dictionaries Working with Trees Understanding the basics of trees Building a tree Representing Relations in a Graph Going beyond trees Building graphs Chapter 7 Arranging and Searching Data Sorting Data Using Merge Sort and Quick Sort Understanding why sorting data is important Employing better sort techniques Using Search Trees and the Heap Considering the need to search effectively Building a binary search tree Performing specialized searches using a binary heap Relying on Hashing Putting everything into buckets Avoiding collisions Creating your own hash function Part 3 Exploring the World of Graphs Chapter 8 Understanding Graph Basics Explaining the Importance of Networks Considering the essence of a graph Finding graphs everywhere Showing the social side of graphs Understanding subgraphs Defining How to Draw a Graph Distinguishing the key attributes Drawing the graph Measuring Graph Functionality Counting edges and vertexes Computing centrality Putting a Graph in Numeric Format Adding a graph to a matrix Using sparse representations Using a list to hold a graph Chapter 9 Reconnecting the Dots Traversing a Graph Efficiently Creating the graph Applying breadth-first search Applying depth-first search Determining which application to use Sorting the Graph Elements Working on Directed Acyclic Graphs (DAGs) Relying on topological sorting Reducing to a Minimum Spanning Tree Getting the minimum spanning tree historical context Working with unweighted versus weighted graphs Creating a minimum spanning tree example Discovering the correct algorithms to use Introducing priority queues Leveraging Prim’s algorithm Testing Kruskal’s algorithm Determining which algorithm works best Finding the Shortest Route Defining what it means to find the shortest path Adding a negative edge Explaining Dijkstra’s algorithm Explaining the Bellman-Ford algorithm Explaining the Floyd-Warshall algorithm Chapter 10 Discovering Graph Secrets Envisioning Social Networks as Graphs Clustering networks in groups Discovering communities Navigating a Graph Counting the degrees of separation Walking a graph randomly Chapter 11 Getting the Right Web page Finding the World in a Search Engine Searching the Internet for data Considering how to find the right data Explaining the PageRank Algorithm Understanding the reasoning behind the PageRank algorithm Explaining the nuts and bolts of PageRank Implementing PageRank Implementing a Python script Struggling with a naive implementation Introducing boredom and teleporting Looking inside the life of a search engine Considering other uses of PageRank Going Beyond the PageRank Paradigm Introducing semantic queries Using AI for ranking search results Part 4 Wrangling Big Data Chapter 12 Managing Big Data Transforming Power into Data Understanding Moore’s implications Finding data everywhere Getting algorithms into business Streaming Flows of Data Analyzing streams with the right recipe Reserving the right data Sketching an Answer from Stream Data Filtering stream elements by heart Demonstrating the Bloom filter Finding the number of distinct elements Learning to count objects in a stream Chapter 13 Parallelizing Operations Managing Immense Amounts of Data Understanding the parallel paradigm Distributing files and operations Employing the MapReduce solution Working Out Algorithms for MapReduce Setting up a MapReduce simulation Inquiring by mapping Chapter 14 Compressing and Concealing Data Making Data Smaller Understanding encoding Considering the effects of compression Choosing a particular kind of compression Choosing your encoding wisely Encoding using Huffman compression Remembering sequences with LZW Hiding Your Secrets with Cryptography Substituting characters Working with AES encryption Part 5 Challenging Difficult Problems Chapter 15 Working with Greedy Algorithms Deciding When It Is Better to Be Greedy Understanding why greedy is good Keeping greedy algorithms under control Considering NP complete problems Finding Out How Greedy Can Be Useful Arranging cached computer data Competing for resources Revisiting Huffman coding Chapter 16 Relying on Dynamic Programming Explaining Dynamic Programming Obtaining a historical basis Making problems dynamic Casting recursion dynamically Leveraging memoization Discovering the Best Dynamic Recipes Looking inside the knapsack Touring around cities Approximating string search Chapter 17 Using Randomized Algorithms Defining How Randomization Works Considering why randomization is needed Understanding how probability works Understanding distributions Simulating the use of the Monte Carlo method Putting Randomness into your Logic Calculating a median using quick select Doing simulations using Monte Carlo Ordering faster with quick sort Chapter 18 Performing Local Search Understanding Local Search Knowing the neighborhood Presenting local search tricks Explaining hill climbing with n-queens Discovering simulated annealing Avoiding repeats using Tabu Search Solving Satisfiability of Boolean Circuits Solving 2-SAT using randomization Implementing the Python code Realizing that the starting point is important Chapter 19 Employing Linear Programming Using Linear Functions as a Tool Grasping the basic math you need Learning to simplify when planning Working with geometry using simplex Understanding the limitations Using Linear Programming in Practice Setting up PuLP at home Optimizing production and revenue Chapter 20 Considering Heuristics Differentiating Heuristics Considering the goals of heuristics Going from genetic to AI Routing Robots Using Heuristics Scouting in unknown territories Using distance measures as heuristics Explaining Path Finding Algorithms Creating a maze Looking for a quick best-first route Going heuristically around by A* Part 6 The Part of Tens Chapter 21 Ten Algorithms That Are Changing the World Using Sort Routines Looking for Things with Search Routines Shaking Things Up with Random Numbers Performing Data Compression Keeping Data Secret Changing the Data Domain Analyzing Links Spotting Data Patterns Dealing with Automation and Automatic Responses Creating Unique Identifiers Chapter 22 Ten Algorithmic Problems Yet to Solve Solving Problems Quickly Solving 3SUM Problems More Efficiently Making Matrix Multiplication Faster Determining Whether an Application Will End Creating and Using One-Way Functions Multiplying Really Large Numbers Dividing a Resource Equally Reducing Edit Distance Calculation Time Playing the Parity Game Understanding Spatial Issues Index EULA