Complete unit-wise study material for AL402 Analysis & Design of Algorithms for RGPV Artificial Intelligence and Machine Learning IV Semester students.
Study all five units covering algorithm complexity, divide and conquer, greedy strategy, dynamic programming, backtracking, branch and bound, NP-complete problems, approximation and parallel algorithms.
Algorithms and complexity, time and space complexity, asymptotic notation, recurrence relations, divide and conquer, binary search, merge sort, quick sort, heap sort, Strassen's matrix multiplication and code optimization techniques.
Greedy method, optimal merge patterns, Huffman coding, minimum spanning trees, knapsack problem, job sequencing with deadlines, single-source shortest path and correctness proofs of greedy algorithms.
Concept of dynamic programming and problems based on this approach including 0/1 knapsack, multistage graph, reliability design and Floyd-Warshall algorithm.
Backtracking, 8-queens problem, Hamiltonian cycle, graph colouring, branch and bound, travelling salesman problem, lower bound theory and introduction to parallel algorithms.
Advanced tree and graph algorithms, NP-hard and NP-complete problems, approximation algorithms, data stream algorithms and design and complexity of parallel algorithms.
Complete unit-wise syllabus for RGPV Artificial Intelligence and Machine Learning IV Semester.
Definitions of algorithms and complexity, Time and Space Complexity; Time space tradeoff, various bounds on complexity, Asymptotic notation, Recurrences and Recurrences solving techniques, Introduction to divide and conquer technique, examples: binary search, merge sort, quick sort, heap sort, Strassen's matrix multiplication etc.
Code tuning techniques: Loop Optimization, Data Transfer Optimization, Logic Optimization etc.
Study of Greedy strategy, examples of greedy method like optimal merge patterns, Huffman coding, minimum spanning trees, knapsack problem, job sequencing with deadlines, single source shortest path algorithm etc. Correctness proof of Greedy algorithms.
Concept of dynamic programming, problems based on this approach such as 0/1 knapsack, multistage graph, reliability design, Floyd-Warshall algorithm etc.
Backtracking concept and its examples like 8 queen's problem, Hamiltonian cycle, Graph colouring problem etc. Introduction to branch & bound method, examples of branch and bound method like travelling salesman problem etc.
Meaning of lower bound theory and its use in solving algebraic problem, introduction to parallel algorithms.
Advanced tree and graph algorithms, NP-hard and NP-complete problems, Approximations Algorithms, Data Stream Algorithms, Introduction to design and complexity of Parallel Algorithm.
AL402 Analysis & Design of Algorithms introduces students to techniques used for designing, analysing and comparing efficient algorithms. The subject covers complexity analysis, divide and conquer, greedy algorithms, dynamic programming, backtracking and advanced computational problems.
Students can access all five units of AL402 in one place and prepare according to the RGPV syllabus using unit-wise handwritten notes.
Select any unit and start your Analysis & Design of Algorithms preparation.
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