Travelling Salesman Problem (Branch & Bound) Part1 | Design and Analysis Of Algorithm (DAA) | Unit 3

Travelling Salesman Problem (Branch & Bound)👆👆👆
0/1 Knapsacks Problem (Branch & Bound): https://youtu.be/GyLCXP7yals
Branch and Bound General Methods: https://youtu.be/cqkDwirjV5Y
Floyd Warshall Algorithm: https://youtu.be/aWqVEzfJ7ss
Travelling Salesman Problem: https://youtu.be/E2UkSvGYWsU
0/1 Knapsacks Problem Example 2: https://youtu.be/eGFrjhVb4yI
0/1 Knapsacks Problem : https://youtu.be/1DiVS6Akw6Q
Longest common Subsequence : https://youtu.be/dKLm9NL2LJM
Dynamic Programming (Matrix Chain Multiplication) : https://youtu.be/gNF4EZ3S3cM
Backtracking(8 queen problem, graph colouring, Hamiltonian Cycle and analysis of these problems): https://youtu.be/fOqOSqiC2T0
Single Source Shortest Path & analysis of these problems : https://youtu.be/k4YSCjQ9dsM
Minimum Spanning Trees (Prim's and Kruskal's Algorithm) : https://youtu.be/8KVpa4XHw7k
Huffman coding : https://youtu.be/frHIzf8sA2c
Job Sequencing with deadlines: https://youtu.be/xv44oX4Cxes
General, Knapsack Problem: https://youtu.be/05cRz6zCdHI
Divide & Conquer: https://youtu.be/SQ-423UoOeI

#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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