Jeff Edmonds received his Ph.D. in 1992 at University of Toronto in theoretical computer science. His thesis proved that certain computation problems require a given amount of time and space. He did his postdoctorate work at the ICSI in Berkeley on secure multi-media data transmission and in 1995 became an Associate Professor in the Department of Computer Science at York University, Canada. He has taught their algorithms course thirteen times to date. He has worked extensively at IIT Mumbai, India, and University of California San Diego. He is well published in the top theoretical computer science journals in topics including complexity theory, scheduling, proof systems, probability theory, combinatorics, and, of course, algorithms.
Inhaltsangabe
Part I. Iterative Algorithms and Loop Invariants: 1. 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 Part II. Recursion: 8. Abstractions, techniques, and theory 9. Some simple examples of recursive algorithms 10. Recursion on trees 11. Recursive images 12. Parsing with context-free grammars Part III. Optimization Problems: 13. Definition of optimization problems 14. Graph search algorithms 15. Network flows and linear programming 16. Greedy algorithms 17. Recursive backtracking 18. Dynamic programming algorithms 19. Examples of dynamic programming 20. Reductions and NP-completeness 21. Randomized algorithms Part IV. Appendix: 22. Existential and universal quantifiers 23. Time complexity 24. Logarithms and exponentials 25. Asymptotic growth 26. Adding made easy approximations 27. Recurrence relations 28. A formal proof of correctness Part V. Exercise Solutions.
Part I. Iterative Algorithms and Loop Invariants: 1. 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 Part II. Recursion: 8. Abstractions, techniques, and theory 9. Some simple examples of recursive algorithms 10. Recursion on trees 11. Recursive images 12. Parsing with context-free grammars Part III. Optimization Problems: 13. Definition of optimization problems 14. Graph search algorithms 15. Network flows and linear programming 16. Greedy algorithms 17. Recursive backtracking 18. Dynamic programming algorithms 19. Examples of dynamic programming 20. Reductions and NP-completeness 21. Randomized algorithms Part IV. Appendix: 22. Existential and universal quantifiers 23. Time complexity 24. Logarithms and exponentials 25. Asymptotic growth 26. Adding made easy approximations 27. Recurrence relations 28. A formal proof of correctness Part V. Exercise Solutions.
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