HOW TO THINK ABOUT ALGORITHMS There are many algorithm texts that provide lots of well-polished code and proofs of correctness. Instead, this one presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. It is a bit like a carpenter studying hammers instead of houses. Jeff Edmonds provides both the big picture and easy step-by-step methods for developing algorithms, while avoiding the comon pitfalls. Paradigms such as loop invariants and recursion help to unify a huge range of algorithms into a few meta-algorithms. Part of the goal is to teach students to think abstractly. Without getting bogged down in formal proofs, the book fosters deeper understanding so that how and why each algorithm works is trans- parent. These insights are presented in a slow and clear manner accessible to second- or third-year students of computer science, preparing them to find on their own innovative ways to solve problems. Abstraction is when you translate the equations, the rules, and the under- lying essences of the problem not only into a language that can be commu- nicated to your friend standing with you on a streetcar, but also into a form that can percolate down and dwell in your subconscious. Because, remem- ber, it is your subconscious that makes the miraculous leaps of inspiration, not your plodding perspiration and not your cocky logic. And remember, unlike you, your subconscious does not understand Java code. Bookmarks Cover Half-title Title Copyright CONTENTS PREFACE Introduction PART ONE: Iterative Algorithms and Loop Invariants 1 Iterative Algorithms: Measures of Progress and Loop Invariants 1.1 A Paradigm Shift: A Sequence of Actions vs. a Sequence of Assertions 1.2 The Steps to Develop an Iterative Algorithm 1.3 More about the Steps 1.4 Different Types of Iterative Algorithms 1.5 Typical Errors 1.6 Exercises 2 Examples Using More-of-the-Input Loop Invariants 2.1 Coloring the Plane 2.2 Deterministic Finite Automaton 2.3 More of the Input vs. More of the Output 3 Abstract Data Types 3.1 Specifications and Hints at Implementations 3.2 Link List Implementation 3.3 Merging with a Queue 3.4 Parsing with a Stack 4 Narrowing the Search Space: Binary Search 4.1 Binary Search Trees 4.2 Magic Sevens 4.3 VLSI Chip Testing 4.4 Exercises 5 Iterative Sorting Algorithms 5.1 Bucket Sort by Hand 5.2 Counting Sort (a Stable Sort) 5.3 Radix Sort 5.4 Radix Counting Sort 6 Euclid’s GCD Algorithm 7 The Loop Invariant for Lower Bounds PART TWO: Recursion 8 Abstractions, Techniques, and Theory 8.1 Thinking about Recursion 8.2 Looking Forward vs. Backward 8.3 With a Little Help from Your Friends 8.4 The Towers of Hanoi 8.5 Checklist for Recursive Algorithms 8.6 The Stack Frame 8.7 Proving Correctness with Strong Induction 9 Some Simple Examples of Recursive Algorithms 9.1 Sorting and Selecting Algorithms 9.2 Operations on Integers 9.3 Ackermann's Function 9.4 Exercises 10 Recursion on Trees 10.1 Tree Traversals 10.2 Simple Examples 10.3 Generalizing the Problem Solved 10.4 Heap Sort and Priority Queues 10.5 Representing Expressions with Trees 11 Recursive Images 11.1 Drawing a Recursive Image from a Fixed Recursive and a Base Case Image 11.2 Randomly Generating a Maze 12 Parsing with Context-Free Grammars PART THREE: Optimization Problems 13 Definition of Optimization Problems 14 Graph Search Algorithms 14.1 A Generic Search Algorithm 14.2 Breadth-First Search for Shortest Paths 14.3 Dijkstra's Shortest-Weighted-Path Algorithm 14.4 Depth-First Search 14.5 Recursive Depth-First Search 14.6 Linear Ordering of a Partial Order 14.7 Exercise 15 Network Flows and Linear Programming 15.1 A Hill-Climbing Algorithm with a Small Local Maximum 15.2 The Primal…Dual Hill-Climbing Method 15.3 The Steepest-Ascent Hill-Climbing Algorithm 15.4 Linear Programming 15.5 Exercises 16 Greedy Algorithms 16.1 Abstractions, Techniques, and Theory 16.2 Examples of Greedy Algorithms 16.2.1 Example: The Job/Event Scheduling Problem 16.2.2 Example: The Interval Cover Problem 16.2.3 Example: The Minimum-Spanning-Tree Problem 16.3 Exercises 17 Recursive Backtracking 17.1 Recursive Backtracking Algorithms 17.2 The Steps in Developing a Recursive Backtracking 17.3 Pruning Branches 17.4 Satisfiability 17.5 Exercises 18 Dynamic Programming Algorithms 18.1 Start by Developing a Recursive Backtracking 18.2 The Steps in Developing a Dynamic Programming Algorithm 18.3 Subtle Points 18.3.1 The Question for the Little Bird 18.3.2 Subinstances and Subsolutions 18.3.3 The Set of Subinstances 18.3.4 Decreasing Time and Space 18.3.5 Counting the Number of Solutions 18.3.6 The New Code 19 Examples of Dynamic Programs 19.1 The Longest-Common-Subsequence Problem 19.2 Dynamic Programs as More-of-the-Input Iterative Loop Invariant Algorithms 19.3 A Greedy Dynamic Program: The Weighted Job/Event Scheduling Problem 19.4 The Solution Viewed as a Tree: Chains of Matrix Multiplications 19.5 Generalizing the Problem Solved: Best AVL Tree 19.6 All Pairs Using Matrix Multiplication 19.7 Parsing with Context-Free Grammars 19.8 Designing Dynamic Programming Algorithms via Reductions 20 Reductions and NP-Completeness 20.1 Satisfiability Is at Least as Hard as Any Optimization Problem 20.2 Steps to Prove NP-Completeness 20.3 Example: 3-Coloring Is NP-Complete 20.4 An Algorithm for Bipartite Matching Using the Network Flow Algorithm 21 Randomized Algorithms 21.1 Using Randomness to Hide the Worst Cases 21.2 Solutions of Optimization Problems with a Random Structure PART FOUR: Appendix 22 Existential and Universal Quantifiers 23 Time Complexity 23.1 The Time (and Space) Complexity of an Algorithm 23.2 The Time Complexity of a Computational Problem 24 Logarithms and Exponentials 25 Asymptotic Growth 25.1 Steps to Classify a Function 25.2 More about Asymptotic Notation 26 Adding-Made-Easy Approximations 26.1 The Technique 26.2 Some Proofs for the Adding-Made-Easy Technique 27 Recurrence Relations 27.1 The Technique 27.2 Some Proofs 28 A Formal Proof of Correctness PART FIVE: Exercise Solutions Chapter 1. Iterative Algorithms: Measures of Progress and Loop Invariants Chapter 2. Examples UsingMore-of-the-Input Loop Invariant Chapter 3. Abstract Data Types Chapter 4. Narrowing the Search Space: Binary Search Chapter 6. Euclid’s GCD Algorithm Chapter 7. The Loop Invariant for Lower Bounds Chapter 8. Abstractions, Techniques, and Theory Chapter 9. Some Simple Examples of Recursive Algorithms Chapter 10. Recursion on Trees Chapter 11. Recursive Images Chapter 12. Parsingwith Context-Free Grammars Chapter 14. Graph Search Algorithms Chapter 15. Network Flows and Linear Programming Chapter 16: Greedy Algorithms Chapter 17. Recursive Backtracking Chapter 18. Dynamic Programming Algorithms Chapter 19. Examples of Dynamic Programs Chapter 20. Reductions and NP-Completeness Chapter 22. Existential and Universal Quantifiers Chapter 23. Time Complexity Chapter 24. Logarithms and Exponentials Chapter 25. Asymptotic Growth Chapter 26. Adding-Made-Easy Approximations Chapter 27. Recurrence Relations CONCLUSION INDEX