Periods/week : 3 Periods & 1 Tut /week.                                                                  Ses. : 30 Exam : 70 Examination (Practical): 3hrs.                                                                                   Credits: 4

Introduction  –  Fundamentals  of  algorithmic  problem  solving  –  important  problem  types  –
fundamental data structures.

 

Fundamentals  of  analysis  of  algorithms  and  efficiency  –  Analysis  framework  –  Asymptotic Notations and Basic Efficiency classes – Mathematical Analysis of  Non-recursive  Algorithms – Mathematical Analysis of recursive Algorithms – Empirical Analysis of Algorithms – Algorithm Visualization
Brute Force  – Selection Sort and Bubble sort – Sequential Search and Brute – Force  String
Matching –  Closest  Pair and Convex-Hull Problems by Brute Force – Exhaustive Search

 

Divide-and-Conquer  – Mergesort – Quicksort – Binary Search – Binary Tree Traversals and Related  Properties  –  Multiplication  of  large  integers  and  Strassen’s  Matrix  Multiplication  – Closest- Pair Convex-Hull Problems by Divide- and – Conquer

 

Decrease – and – Conquer – Insertion Sort – Depth-First Search and Breadth-First Search- Topological  Sorting  –  Algorithms  for  Generating  Combinatorial  Objects  –  Decrease-by-a- Constant-Factor Algorithms – Variable-Size-Decrease Algorithms

 

Transform-and-Conquer – Presorting – Gaussian Elimination – Balanced Search Trees – Heaps and Heapsort – Horner’s Rule and Binary Exponentiation – Problem Reduction

 

Space  and Time Tradeoffs – Sorting by Counting – Input Enhancement in string Matching – Hashing – B-Trees

 

Dynamic Programming – Computing a Binomial Coefficient – Warshall’s and Floyd’s Algorithm
– Optimal Binary Search Trees – The Knapsack Problem and Memory Functions.

Greedy Technique – Prim’s Algorithm – Kruskal’s Algorithm – Dijkstra’s Algorithm –

Huffman Trees Limitations of Algorithm Power – Lower-Bound Arguments – Decision Trees – P, NP and NP – complete problems – Challenges of Numerical Algorithms

 

Coping  with  the  Limitations  of  Algorithms  Power  –  Backtracking  –  Branch-and-Bound  – Approximation Algorithms for NP-hard Problems – Algorithms for solving Nonlinear Equations.

 

Text Book:
Introduction to Design & Analysis of Algorithms by Anany Levitin, Pearson Education, New
Delhi, 2003
Reference Books:
1.    Introduction to Algorithms by  Thomas H. Corman, Charles E. Leiserson, Ronald R. Rivest & Clifford Stein, Prentice Hall of India, New Delhi, New Delhi
2.     The  Design  and  Analysis  of  computer  Algorithms,  Aho,  Hopcroft  &  Ullman,       Pearson
EducationNew Delhi, 2003
3.     Fundamentals of algorithmics, Gilles Brassard & Paul Bratley, Prentice Hall of India, New

Delhi

tejus mahiCSE 3.2 SyllabusIT 3.2 SyllabusCSE,CSE Syllabus,Design And Analysis Of Algorithms Syllabus,IT,IT Syllabus
Periods/week : 3 Periods & 1 Tut /week.                                                                  Ses. : 30 Exam : 70 Examination (Practical): 3hrs.                                                                                   Credits: 4 Introduction  –  Fundamentals  of  algorithmic  problem  solving  –  important  problem  types  – fundamental data structures.   Fundamentals  of  analysis...