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- Design And Analysis Of Algorithm - DAA
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**GIET UNIVERSITY - GIETU**- Computer Science Engineering
- 14 Topics
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Page-1

- Syllabus - ( 1 - 1 )
- Introduction to Algorithm, Application of Algorithm - ( 2 - 4 )
- Asymptotic analysis - ( 5 - 12 )
- Recurrences - ( 13 - 18 )
- Master methods - ( 19 - 20 )
- Amortized analysis - ( 21 - 22 )
- Divide-and- conquer Approach - ( 23 - 39 )
- Dynamic programming methodology - ( 40 - 53 )
- Greedy Algorithms - ( 54 - 61 )
- Data structure for disjoint sets - ( 62 - 65 )
- Graph Algorithms - ( 66 - 81 )
- Back tracking, Branch and Bound - ( 82 - 95 )
- NP - Completeness - ( 96 - 98 )
- Approximation algorithms - ( 99 - 102 )

Topic:

SYLLABUS DESIGN AND ANALYSIS OF ALGORITHMS Pre -Requisite: Data Structure and C/C++ Course Educational Objective CEO1: Analyze the asymptotic performance of algorithms CEO2: Demonstrate a familiarity with major algorithms CEO3: Apply important algorithmic design paradigms and methods of analysis CEO4: Synthesize efficient algorithms in common engineering design situations Course Outcome: At the end of the course, the student will be capable of CO1 Explain worst-case running times of algorithms using asymptotic analysis CO2 Apply the algorithms and design techniques to solve problems. CO3 Apply the algorithms and design techniques to find the optimal solution. CO4 Predict the approximation algorithm for time consuming problem. UNIT:1 Introduction (15 Hours) Definition, Characteristic of algorithm, Growth of Functions, Asymptotic analysis, Amortized analysis, standard notations and common functions, limit theorem, Stirling's formula. Recurrences: solution of recurrences by substitution, recursion tree and Master methods, Extension Master Methods. Algorithm design techniques. UNIT:2 (15 Hours) Divide-and- conquer Approach: Binary search, Quick sort, Merge sort, Heap Sort, Priority Queue, Lower bounds for sorting. Worst case analysis of Quick sort. Dynamic programming methodology: Elements of dynamic programming, Matrix-chain multiplication, Longest common subsequence, Assembly-line scheduling. Greedy Algorithms: Elements of Greedy strategy, Activity selection Problem, Fractional knapsack problem, Huffman codes. UNIT:3 (10 Hours) Graph Algorithms: Data structure for disjoint sets, Disjoint set operations, Linked list representation, path compression, Disjoint set forests. Graph Algorithms and their characteristics, Breadth first search and depth-first search, Minimum Spanning Trees, Kruskal algorithm and Prim's algorithms, single- source shortest paths (Bellman-ford Algorithm and Dijkstra's algorithms), Allpairs shortest paths (Floyd–Warshall Algorithm). UNIT:4 (10 Hours) Back tracking, Branch and Bound, Eight Queen problem, Sub Set Sum Problem. String matching algorithms, naïve string matching algorithm, Rabin-Karp algorithm, Knuth–Morris–Pratt algorithm, NP - Completeness (Polynomial time, Polynomial time verification, NP - Completeness and reducibility, NP-Complete problems (without Proofs), Approximation algorithms characteristics, Traveling Salesman Problem, vertex Cover Problem.

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