How to Think about Algorithms
Autor Jeff Edmondsen Limba Engleză Paperback – 7 mar 2024
Preț: 234.68 lei
Preț vechi: 293.34 lei
-20%
Puncte Express: 352
Carte tipărită la comandă
Livrare economică 11-25 iulie
Livrare express 06-12 iunie pentru 102.35 lei
Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit de la 400.00 lei Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
Specificații
ISBN-13: 9781009302135
ISBN-10: 1009302132
Pagini: 464
Dimensiuni: 170 x 244 x 33 mm
Greutate: 1.05 kg
Ediția:2. Auflage
Editura: Cambridge University Pr.
Locul publicării:Cambridge, United Kingdom
ISBN-10: 1009302132
Pagini: 464
Dimensiuni: 170 x 244 x 33 mm
Greutate: 1.05 kg
Ediția:2. Auflage
Editura: Cambridge University Pr.
Locul publicării:Cambridge, United Kingdom
Cuprins
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.
Descriere
Exceptionally student-friendly, now with over 150 new exercises, key concept summaries, and a new chapter on machine learning algorithms.