Professional Courses
Industry-relevant training in Business, Technology, and Design
Categories
Interactive Games
Fun games to boost memory, math, typing, and English skills
Typing
Memory
Math
English Adventures
Knowledge
Design & Analysis of Algorithms - Vol 2
The chapter discusses memoization and dynamic programming as strategies to optimize recursive computations, particularly in the context of defining functions like Fibonacci. Memoization prevents redundant calculations by storing previously computed results, while dynamic programming eliminates recursion by systematically filling in values based on identified dependencies. Through these strategies, computational efficiency improves significantly, addressing the challenges of overlapping subproblems in recursive definitions.
24 Chapters
12 weeks
You've not yet enrolled in this course. Please enroll to listen to audio lessons, classroom
podcasts and take practice test.
Course Chapters
Chapter 1
All-pairs Shortest Paths
Chapter 2
Minimum Cost Spanning Trees
Chapter 3
Spanning Trees: Prim’s Algorithm
Chapter 4
Dijkstra's Algorithm and Prim's Algorithm
Chapter 5
Kruskal's Algorithm
Chapter 6
Union-Find Data Structure
Chapter 7
Union-Find Data Structure Using Pointers
Chapter 8
Priority Queues
Chapter 9
Heaps
Chapter 10
Height of the Heap
Chapter 11
Heaps and Dijkstra's Algorithm
Chapter 12
Divide and Conquer: Counting Inversions
Chapter 13
Divide and Conquer: Closest Pair of Points
Chapter 14
Search Trees
Chapter 15
Find Operations
Chapter 16
Insertion in a Search Tree
Chapter 17
Balanced Search Trees
Chapter 18
AVL Tree Rotations
Chapter 19
Greedy algorithms: Interval scheduling
Chapter 20
Greedy Algorithms: Minimizing Lateness
Chapter 21
Greedy Algorithms: Huffman Codes
Chapter 22
Introduction to Recursive Solutions and Huffman Coding
Chapter 23
Dynamic Programming
Chapter 24