Description: Design and Analysis of Algorithms is technique for designing and implementing algorithm designs such as template method pattern and decorator pattern, and uses of data structures, and name and sort lists.
Curriculum
- 1 Section
- 33 Lessons
- 10 Weeks
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- Design & Analysis of Algorithms33
- 2.1Lecture 1: Overview
- 2.2Lecture 2: Framework for Algorithms Analysis
- 2.3Lecture 3: Algorithms Analysis Framework – II
- 2.4Lecture 4: Asymptotic Notation
- 2.5Lecture 5: Algorithm Design Techniques : Basics
- 2.6Lecture 6: Divide And Conquer – I
- 2.7Lecture 7: Divide And Conquer -II Median Finding
- 2.8Lecture 8: Divide And Conquer -III Surfing Lower Bounds
- 2.9Lecture 9: Divide And Conquer -IV Closest Pair
- 2.10Lecture 10: Greedy Algorithms – I
- 2.11Lecture 11: Greedy Algorithms – II
- 2.12Lecture 12: Greedy Algorithms – III
- 2.13Lecture 13: Greedy Algorithms – IV
- 2.14Lecture 14: Pattern Matching – I
- 2.15Lecture 15: Pattern Matching – II
- 2.16Lecture 16: Combinational Search and Optimization I
- 2.17Lecture 17: Combinational Search and Optimization II
- 2.18Lecture 18: Dynamic Programming
- 2.19Lecture 19: Longest Common Subsequences
- 2.20Lecture 20: Matric Chain Multiplication
- 2.21Lecture 21: Scheduling with Startup and Holding Costs
- 2.22Lecture 22: Bipartite Maximum Matching
- 2.23Lecture 23: Lower Bounds for Sorting
- 2.24Lecture 24: Element Distinctness Lower Bounds
- 2.25Lecture 25: NP-Completeness-I -Motivation
- 2.26Lecture 26: NP – Completeness- II
- 2.27Lecture 27: NP – Completeness- III
- 2.28Lecture 28: NP – Completeness- IV
- 2.29Lecture 29: NP – Completeness- V
- 2.30Lecture 30: NP – Completeness- VI
- 2.31Lecture 31: Approximation Algorithms
- 2.32Lecture 32: Approximation Algorithms
- 2.33Lecture 33: Approximation Algorithms for NP
