Jeff Edmonds
How to Think about Algorithms
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Jeff Edmonds
How to Think about Algorithms
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Textbook that teaches students how to think about algorithms like an expert, without getting bogged down in formal proof.
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Textbook that teaches students how to think about algorithms like an expert, without getting bogged down in formal proof.
Produktdetails
- Produktdetails
- Verlag: Cambridge-Hitachi
- Seitenzahl: 472
- Erscheinungstermin: 19. Mai 2008
- Englisch
- Abmessung: 254mm x 178mm x 25mm
- Gewicht: 866g
- ISBN-13: 9780521614108
- ISBN-10: 0521614104
- Artikelnr.: 23549925
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- 06621 890
- Verlag: Cambridge-Hitachi
- Seitenzahl: 472
- Erscheinungstermin: 19. Mai 2008
- Englisch
- Abmessung: 254mm x 178mm x 25mm
- Gewicht: 866g
- ISBN-13: 9780521614108
- ISBN-10: 0521614104
- Artikelnr.: 23549925
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- 06621 890
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.
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.
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.
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.