 JNTUH B.TECH R18 4-1 Syllabus For Computational complexity PDF 2022

Get Complete Lecture Notes for Computational complexity on Cynohub APP  You will be able to find information about Computational complexity along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Computational complexity after reading this blog. Computational complexity has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Computational complexity can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to Computational complexity are mentioned below in detail. If you are having a hard time understanding Computational complexity or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

Computational complexity Unit One

UNIT – I

Computational Complexity: Polynomial time and its justification, Nontrivial examples of polynomial-time algorithms, the concept of reduction (reducibility), Class P Class NP and NP- Completeness, The P versus NP problem and why it’s hard

Computational complexity Unit Two

UNIT – II

Algorithmic paradigms: Dynamic Programming – Longest common subsequence, matrix chain multiplication, knapsack problem, Greedy – 0-1 knapsack, fractional knapsack, scheduling problem, Huffman coding, MST, Branch-and-bound – travelling sales person problem, 0/1 knapsack problem, Divide and Conquer – Merge sort, binary search, quick sort.

Get Complete Lecture Notes for Computational complexity on Cynohub APP  Computational complexity Unit Three

UNIT – III

Randomized Algorithms: Finger Printing, Pattern Matching, Graph Problems, Algebraic Methods, Probabilistic Primality Testing, De-Randomization Advanced Algorithms.

Computational complexity Unit Four

UNIT – IV

Graph Algorithms: Shortest paths, Flow networks, Spanning Trees; Approximation algorithms, Randomized algorithms. Approximation algorithms: Polynomial Time Approximation Schemes.

Computational complexity Unit Five

UNIT – V

Advanced Data Structures and applications: Decision Trees and Circuits, B-Trees, AVL Trees, Red and Black trees, Dictionaries and tries, Maps, Binomial Heaps, Fibonacci Heaps, Disjoint sets, Union by Rank and Path Compression.

Computational complexity Course Objectives

Introduces to theory of computational complexity classes
Discuss about algorithmic techniques and application of these techniques to problems.
Introduce to randomized algorithms and discuss how effective they are in reducing time and space complexity.
Discuss about Graph based algorithms and approximation algorithms

Computational complexity Course Outcomes

Ability to classify decision problems into appropriate complexity classes
Ability to specify what it means to reduce one problem to another, and construct reductions for simple examples.
Ability to classify optimization problems into appropriate approximation complexity classes
Ability to choose appropriate data structure for the given problem
Ability to choose and apply appropriate design method for the given problem

Computational complexity Text Books

T. Cormen, C. Leiserson, R. Rivest and C. Stein, Introduction to Algorithms, Third Edition, McGraw-Hill, 2009.
R. Motwani and P. Raghavan, Randomized Algorithms, Cambridge University Press, 1995.
J. J. McConnell, Analysis of Algorithms: An Active Learning Approach, Jones & Bartlett Publishers, 2001.
D. E. Knuth, Art of Computer Programming, Volume 3, Sorting and Searching, Second Edition, Addison-Wesley Professional, 1998.
S. Dasgupta, C. H. Papadimitriou and U. V. Vazirani, Algorithms, McGraw-Hill, 2008.

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Scoring Marks in Computational complexity  