The second edition of this student-friendly textbook now includes over 150 new exercises, key concept summaries and a chapter on machine learning algorithms. Its approachability and clarity make it ideal as both a main course text or as a supplementary book for students who find other books challenging.
The second edition of this student-friendly textbook now includes over 150 new exercises, key concept summaries and a chapter on machine learning algorithms. Its approachability and clarity make it ideal as both a main course text or as a supplementary book for students who find other books challenging.
Jeff Edmonds is Professor in the Department of Electrical Engineering and Computer Science at York University, Canada.
Inhaltsangabe
Preface Introduction Part I. Iterative Algorithms and Loop Invariants: 1. Iterative algorithms: measures of progress and loop invariants 2. Examples using more-of-the-input loop invariant 3. Abstract data types 4. Narrowing the search space: binary search 5. Iterative sorting algorithms 6. Euclid's GCD algorithm 7. The loop invariant for lower bounds 8. Key concepts summary: loop invariants and iterative algorithms 9. Additional exercises: Part I 10. Partial solutions to additional exercises: Part I Part II. Recursion: 11. Abstractions, techniques, and theory 12. Some simple examples of recursive algorithms 13. Recursion on trees 14. Recursive images 15. Parsing with context-free grammars 16. Key concepts summary: recursion 17. Additional exercises: Part II 18. Partial solutions to additional exercises: Part II Part III. Optimization Problems: 19. Definition of optimization problems 20. Graph search algorithms 21. Network flows and linear programming 22. Greedy algorithms 23. Recursive backtracking 24. Dynamic programming algorithms 25. Examples of dynamic programming 26. Reductions and NP-completeness 27. Randomized algorithms 28. Key concepts summary: greedy algorithms and dynamic programmings 29. Additional exercises: Part III 30. Partial solutions to additional exercises: Part III Part IV. Additional Topics: 31. Existential and universal quantifiers 32. Time complexity 33. Logarithms and exponentials 34. Asymptotic growth 35. Adding-made-easy approximations 36. Recurrence relations 37. A formal proof of correctness 38. Additional exercises: Part IV 39. Partial solutions to additional exercises: Part IV Exercise Solutions Conclusion Index.
Preface Introduction Part I. Iterative Algorithms and Loop Invariants: 1. Iterative algorithms: measures of progress and loop invariants 2. Examples using more-of-the-input loop invariant 3. Abstract data types 4. Narrowing the search space: binary search 5. Iterative sorting algorithms 6. Euclid's GCD algorithm 7. The loop invariant for lower bounds 8. Key concepts summary: loop invariants and iterative algorithms 9. Additional exercises: Part I 10. Partial solutions to additional exercises: Part I Part II. Recursion: 11. Abstractions, techniques, and theory 12. Some simple examples of recursive algorithms 13. Recursion on trees 14. Recursive images 15. Parsing with context-free grammars 16. Key concepts summary: recursion 17. Additional exercises: Part II 18. Partial solutions to additional exercises: Part II Part III. Optimization Problems: 19. Definition of optimization problems 20. Graph search algorithms 21. Network flows and linear programming 22. Greedy algorithms 23. Recursive backtracking 24. Dynamic programming algorithms 25. Examples of dynamic programming 26. Reductions and NP-completeness 27. Randomized algorithms 28. Key concepts summary: greedy algorithms and dynamic programmings 29. Additional exercises: Part III 30. Partial solutions to additional exercises: Part III Part IV. Additional Topics: 31. Existential and universal quantifiers 32. Time complexity 33. Logarithms and exponentials 34. Asymptotic growth 35. Adding-made-easy approximations 36. Recurrence relations 37. A formal proof of correctness 38. Additional exercises: Part IV 39. Partial solutions to additional exercises: Part IV Exercise Solutions Conclusion Index.
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