Subject Code & Name: CS25C12 – Algorithms
Regulation: R-2025
Semester: IV (Fourth Semester)
Branch: B.E. Computer Science and Engineering (CSE) / B.Tech. Artificial Intelligence and Data Science (AI&DS) / B.E. Computer Science and Engineering (Data Science) (CSE(DS)) / B.E. Computer Science and Engineering (Internet of Things) (CSE(IoT)) / B.E. Computer Science and Engineering (Cyber Security) (CSE(Cyber)) / B.E. Computer Science and Engineering (Artificial Intelligence and Machine Learning) (CSE(AI&ML)) / B.E. Computer Science and Design (CSD)
Credits / L-T-P: 3 Credits | L-T-P: 3-0-0
Course Objectives
- This course aims at providing the fundamentals of algorithm design and analysis and explains the concepts greedy method and dynamic programming.
- Also, this course illustrates the methods of backtracking and branch bound techniques to solve the problems.
Full Unit-wise Syllabus
Unit I – Foundations of Algorithm Analysis
Performance analysis – space complexity, time complexity, asymptotic notation – big (O) notation, omega notation, theta notation and little (o) notation, recurrences, probabilistic analysis.
Activities: Assignment: Time and Space Complexity calculation for a given Logic; Usage of loops and conditionals and mathematical principles.
Unit II – Divide & Conquer Methods
Merge sort, Long Integer Multiplication, Strassen’s matrix multiplication, Master method, Job Sequencing Problem with Deadlines.
Activities: Flipped Class Room: Solving a puzzle; Review of GATE Questions.
Unit III – Greedy Methods and Dynamic Programming
Activity Selection Problem, Huffman Codes and Knapsack fractional. Dynamic Programming Method: Knapsack 0 – 1, Matrix Chain Multiplication, Optimal Binary Search Tree and Longest Common Subsequence.
Activities: Project based experiential learning: Creation of treasure hunt; Quiz: State Transition and Recurrence Relations – Space Optimization Technique.
Unit IV – String Matching and Convex Hull Algorithms
Multithreaded algorithms, Polynomial Multiplication, Fast Fourier Transform, Extended Euclid Algorithm. Naïve’s algorithm, Rabin Karp algorithm – Graham’s Scan and Jarvi’s March method.
Activities: Assignment: Rabin Karp algorithm; Quiz: Multithreaded and Euclid algorithms.
Unit V – Solvability and Tractability
The classes P and NP, NP Hard and NP Complete Problems. vertex-cover, travelling-salesman, set-covering, subset-sum Problem – N Queen Problem, Graph Coloring, Hamiltonian Cycle Problem – Assignment Problem, Travelling Salesman and Knapsack Problem.
Activities: Assignment: Approximation and randomized Algorithms; Review of GATE Questions.
Unit VI – Network Flow Algorithms
Flow Networks, Maximum Flows: Ford-Fulkerson, Edmond-Karp, Push relabel Algorithm, Relabel-to-front algorithm, Minimum Cost flows, Cycle Cancelling Algorithm.
Activities: Flipped Class Room: Relabel Algorithm; Review of GATE Questions.
Course Outcomes (COs)
- CO1: Describe the fundamental concepts of algorithms for solving computational problems.
- CO2: Analyze various algorithmic approaches and dynamic programming to understand their efficiency and applicability.
- CO3: Evaluate algorithms based on time and space complexity to determine their effectiveness for different problem scenarios.
- CO4: Design appropriate design paradigms and optimization techniques to solve real-world computational problems.
- CO5: Develop the ability to apply advanced algorithmic techniques through continuous self-learning.
Assessment Pattern (Quick Note)
- Weightage: Continuous Assessment 40% | End Semester Theory Examination 60%
- Internal methodology: Activities 30% (Assignments 30, Quiz 10, Project based learning 25, Flipped Classroom 10, Review of GATE questions 25), Internal Examinations 70% (TWO tests)
Source: Official Anna University – B.E. Computer Science and Engineering R-2025 Syllabus
Last Updated: October 2026
Comments
Post a Comment