Travelling Salesman Problem | Dynamic programming | Design and Analysis Of Algorithms (DAA)| Unit 3

Travelling Salesman Problem👆👆👆
0/1 Knapsacks Problem Example 2:    • 0/1 Knapsacks Problem Part 2 | Dynamic pro...  
0/1 Knapsacks Problem :    • 0/1 Knapsacks Problem | Dynamic programmin...  
Longest common Subsequence :    • Longest common Subsequence | Dynamic progr...  
Dynamic Programming (Matrix Chain Multiplication) :    • Dynamic programming | Matrix Chain Multipl...  
Backtracking(8 queen problem, graph colouring, Hamiltonian Cycle and analysis of these problems):    • Backtracking All Applications | Design and...  
Single Source Shortest Path & analysis of these problems :    • Single Source Shortest Path | Design and A...  
Minimum Spanning Trees (Prim's and Kruskal's Algorithm) :    • Minimum Spanning Trees | Design and Analys...  
Huffman coding :    • Huffman Coding | Design and Analysis Of Al...  
Job Sequencing with deadlines:    • Job Sequencing with Deadlines | Design and...  
General, Knapsack Problem:    • Greedy Method: General, Knapsack Problem |...  
Divide & Conquer:    • Divide & Conquer (All Methods Complete) | ...  
#ipu #mait #engineering #education #huffman #huffmancoding #algorithmanalysis
Design and Analysis Of Algorithms (DAA)
All units Covered
Unit 1, Unit 2, Unit 3, Unit 4
Best suitable for IPU Students and IPU Syllabus
GGSIPU
AKTU
MAIT
MSIT
BVCOE
BPIT
GNIOT
GTBIT
ADGITM
HMRITM
SAITM
...
TOPICS COVERED
UNIT‐I
Asymptotic notations for time and space complexity, Methods for solving Recurrence relations, Brief Review of Graphs, Sets and disjoint sets, union, sorting and searching algorithms and their analysis in terms of space and time complexity.
Divide and Conquer: General method, binary search, merge sort, Quick sort, selection sort, Strassen’s matrix multiplication algorithms and analysis of algorithms for these problems.
UNIT‐II
Greedy Method: General method, knapsack problem, Huffman Codes, job sequencing with deadlines, minimum spanning trees, single souce paths and analysis of these problems.
Back Tracking: General method, 8 queen’s problem, graph colouring, Hamiltonian cycles, and analysis of these problems.
UNIT‐III
Dynamic Programming: Ingredients of Dynamic Programming. Matrix Chain Multiplication, Longest common subsequence and optimal binary search trees problems, 0‐1 knapsack problem, Traveling salesperson problem, Floyd Warshall algorithm.
Branch and Bound: Method, O/I knapsack and traveling salesperson problem
UNIT ‐ IV
String Matching: The naïve String Matching algorithm, The Rabin‐Karp Algorithm, String Matching with finite automata, The Knuth‐Morris Pratt algorithm.
Computational Complexity: Basic Concepts, Polynomial vs Non‐Polynomial Complexity, NP‐ hard & NP‐ complete classes. Approximation Algorithms
Flow and Sorting Network: Ford‐ Fulkerson method, Maximum bipartite matching, Sorting Networks, Comparison network, Zero‐ one principle, Bitonic sorting network, merging network

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