Project: Coursera-Stanford-Divide-and-Conquer-Sorting-and-Searching-and-Randomized-Algorithms (GitHub Link)

Coursera-Stanford-Divide-and-Conquer-Sorting-and-Searching-and-Randomized-Algorithms

Course can be found in Coursera

Quiz answers and notebook for quick search can be found in my blog SSQ

Week 1 Lecture slides:
- 1: Divide and Conquer:
- Integer Multiplication;
- Karatsuba Multiplication;
- [x] Implementation by Python;
- Merge Sort.
- 2: Asymptotic Analysis:
- Big-oh notation; Omega and Theta notation;
Week 2 Lecture slides:
- 3: Divide and Conquer:
- Counting Inversions
- [x] Implementation by python;
- Matrix Multiplication Strassen’s Algorithm
- Closest Pair Optional
- 4: The Master Method:
- Motivation; Example; Proof I, Proof II
Week 3 Lecture slides:
- 5: Randomized Algorithm - QuickSort:
- Overview; The Partition Subroutine; Proof; Choosing a Good Pivot
- [x] Implementation of QuickSort with 3 pivots by python
- 6: QuickSort Analysis:
- the average running time of QuickSort (with random pivots)
- 7: Probability Review:
Week 4 Lecture slides:
- 8: Linear-time Selection
- Randomized Selection
- Deterministic Selection
- 9: Graphs and The Minimum Cut
- Random Contraction Algorithm
- [x] Implementation by Python