NOC:Design and Analysis of Algorithms - Study24x7

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NOC:Design and Analysis of Algorithms

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Description

The analysis of algorithms is the determination of the computational complexity of algorithms, that is the amount of time, storage and/or other resources necessary to execute them. Usually, this involves determining a function that relates the length of an algorithm's input to the number of steps it takes (its time complexity) or the number of storage locations it uses (its space complexity). An algorithm is said to be efficient when this function's values are small, or grow slowly compared to a growth in the size of the input. Different inputs of the same length may cause the algorithm to have different behavior, so best, worst and average case descriptions might all be of practical interest. When not otherwise specified, the function describing the performance of an algorithm is usually an upper bound, determined from the worst case inputs to the algorithm.

Includes

• Total 8 Modules
• 56 Videos
• Published on 08 July, 2019

Week 1-Introduction, Analysis of Algorithms

• Course Outline

17m
• Example: Air Travel

9m
• Example: Xerox shop

6m
• Example: Document similarity

9m
• Introduction and motivation

18m
• Input size, worst case, average case

10m
• Quantifying efficiency: O( ), Omega( ), Theta( )

18m
• Examples: Analysis of iterative and recursive algorithms

17m

Week 2-Searching and sorting

• Insertion sort

13m
• Selection Sort

13m
• Searching in an array

13m
• Arrays and lists

13m
• Sorting - Concluding remarks

8m
• Quicksort - analysis

8m
• Quicksort

8m
• Merge sort - analysis

8m
• Merge sort

8m

Week 3-Graphs

• Representing graphs

13m
• Introduction to graphs

13m
• Directed acylic graphs: longest paths

14m
• Directed acylic graphs: topological sort

14m
• Applications of BFS and DFS

14m
• Depth first search (DFS)

14m

14m

Week 4-Weighted graphs

• Kruskals algorithm

14m
• Prims Algorithm

14m
• Minimum Cost Spanning Trees

14m
• All pairs shortest paths

14m
• Negative edge weights: Bellman-Ford algorithm

14m
• Dijkstras algorithm: analysis

14m
• Single source shortest paths: Dijkstras algorithm

14m

Week 5- Data Structures: Union-Find and Heaps, Divide and Conquer

• Closest pair of points

16m
• Counting inversions

16m
• Heaps: Updating values, sorting

16m
• Heaps

16m
• Priority queues

16m
• Union-Find using pointers

16m
• Union-Find using arrays

16m

Week 6-Data Structures: Search Trees, Greedy Algorithms

• Huffman codes

29m
• Scheduling with deadlines: minimizing lateness

29m
• Interval scheduling

29m
• Balanced search trees

29m
• Binary Search Trees

29m

Week 7-Dynamic Programming

• Matrix multiplication

15m
• Edit distance

15m
• Common subwords and subsequences

15m
• Grid Paths

15m
• Memoization

15m
• Introduction to dynamic programming

15m

Week 8-Linear Programming and Network Flows,Intractability

• P and NP

20m
• Checking Algorithms

20m
• Reductions

20m
• Network Flows

20m
• LP modelling: Bandwidth allocation

20m
• LP modelling: Production Planning

20m
• Linear Programming

20m

Review & Ratings

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08 Jul 2019 05:37 PM

Excellent

Nice course to learn.

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