divide conquer

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1. 1▶Packing and Covering Paths (Lecture 14, Fundamental Algorithms, Spring 2022)Fundamental Algorithms, Spring 2022Notes
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2. 2▶Spanning trees (Fundamental algorithms, Spring 2022, Lecture 13)Fundamental Algorithms, Spring 2022Notes
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3. 3▶Divide and conquer II (Fundamental algorithms, Spring 2022, Lecture 12)Fundamental Algorithms, Spring 2022Notes
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4. 4▶Midterm 1 Review (Fundamental Algorithms, Spring 2022)Fundamental Algorithms, Spring 2022Notes
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5. 5▶Divide and conquer (Fundamental Algorithms, Spring 2022, Lecture 11)Fundamental Algorithms, Spring 2022Notes
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6. 6▶Negative edge lengths and all pairs shortest paths (Fundamental Algorithms, Spring 2022, Lecture 9)Fundamental Algorithms, Spring 2022Notes
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7. 7▶NP-Completeness (Fundamental Algorithms, Spring 2022, Lecture 10)Fundamental Algorithms, Spring 2022Notes
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8. 8▶Shortest walks (Fundamental Algorithms, Lecture 8, Spring 2022)Fundamental Algorithms, Spring 2022Notes
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9. 9▶The longest path (Fundamental Algorithms, Spring 2022, Lecture 7)Fundamental Algorithms, Spring 2022Notes
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10. 10▶Searching and Sorting Graphs (Fundamental Algorithms, Spring 2022, Lecture 6)Fundamental Algorithms, Spring 2022Notes
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11. 11▶Optimization in graphs (Fundamental Algorithms, Spring 2022, Lecture 5)Fundamental Algorithms, Spring 2022Notes
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12. 12▶Sequential Optimization (Fundamental Algorithms, Spring 2022, Lecture 4)Fundamental Algorithms, Spring 2022Notes
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13. 13▶Subset Sum (Fundamental Algorithms, Spring 2022, Lecture 3)Fundamental Algorithms, Spring 2022Notes
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14. 14▶Fundamental Algorithms, Spring 2022, Lecture 01: Searching and sortingFundamental Algorithms, Spring 2022Notes
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15. 15▶SAT (Fundamental Algorithms, Spring 2022, Lecture 2)Fundamental Algorithms, Spring 2022Notes
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16. 16▶CSE201, Lec 15: Karatsuba multiplication and Strassen's matrix multiplicationCSE201, Winter 2022: Analysis of AlgorithmsNotes
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17. 17▶CSE201, Lec 14: Linear time selection using divide and conquerCSE201, Winter 2022: Analysis of AlgorithmsNotes
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18. 18▶CSE201, Lec 13: Introduction to divide and conquer, Mergesort and the stock market problemCSE201, Winter 2022: Analysis of AlgorithmsNotes
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19. 19▶CSE201, Lec 12: Dijkstra's algorithms, All Pairs Shortest Paths, and matrix multiplicationCSE201, Winter 2022: Analysis of AlgorithmsNotes
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20. 20▶CSE201, Lec 11: Single source shortest paths and the Bellman-Ford algorithmCSE201, Winter 2022: Analysis of AlgorithmsNotes
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21. 21▶CSE201, Lec 10: Minimum Spanning TreesCSE201, Winter 2022: Analysis of AlgorithmsNotes
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22. 22▶CSE201, Lec 9: Huffman codingCSE201, Winter 2022: Analysis of AlgorithmsNotes
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23. 23▶CSE201, Lec 8: Greedy Algorithms; the Fractional Knapsack Problem and introduction to Huffman CodingCSE201, Winter 2022: Analysis of AlgorithmsNotes
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24. 24▶CSE201, Lec 7: Dynamic Programming for the Longest Common Subsequence and Optimal BST problemsCSE201, Winter 2022: Analysis of AlgorithmsNotes
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25. 25▶CSE201, Lec 6: Intro to Dynamic ProgrammingCSE201, Winter 2022: Analysis of AlgorithmsNotes
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26. 26▶CSE201, Lec 5: Using Depth-First Search to find Strongly Connected ComponentsCSE201, Winter 2022: Analysis of AlgorithmsNotes
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27. 27▶CSE201, Lec 4: More on Depth-First Search, Topologically sorting a DAGCSE201, Winter 2022: Analysis of AlgorithmsNotes
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28. 28▶CSE201, Lec 3: Depth First Search (DFS) and the concept of finishing timesCSE201, Winter 2022: Analysis of AlgorithmsNotes
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29. 29▶CSE201, Lec 2: More on the BFS invariant, and using BFS for 2-coloring/bipartitenessCSE201, Winter 2022: Analysis of AlgorithmsNotes
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30. 30▶CSE201, Lec 1:The Breadth First Search (BFS) invariantCSE201, Winter 2022: Analysis of AlgorithmsNotes
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31. 31▶5. Amortization: Amortized AnalysisMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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32. 32▶R5. Dynamic ProgrammingMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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33. 33▶11. Dynamic Programming: All-Pairs Shortest PathsMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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34. 34▶R1. Matrix Multiplication and the Master TheoremMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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35. 35▶8. Randomization: Universal & Perfect HashingMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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36. 36▶9. Augmentation: Range TreesMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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37. 37▶6. Randomization: Matrix Multiply, QuicksortMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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38. 38▶R4. Randomized Select and Randomized QuicksortMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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39. 39▶3. Divide & Conquer: FFTMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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40. 40▶1. Course Overview, Interval SchedulingMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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41. 41▶4. Divide & Conquer: van Emde Boas TreesMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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42. 42▶10. Dynamic Programming: Advanced DPMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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43. 43▶R2. 2-3 Trees and B-TreesMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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44. 44▶2. Divide & Conquer: Convex Hull, Median FindingMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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45. 45▶7. Randomization: Skip ListsMIT 6.046J Design and Analysis of Algorithms, Spring 2015Notes
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