Dynamic programming – Principle of optimality

Approximation Algorithms for NP Hard Problems Travelling Salesman problem Knapsack problem

Approximation Algorithms for NP Hard Problems – Travelling Salesman problem – Knapsack problem

LIFO Search and FIFO search Knapsack Problem || Branch and Bound

Travelling Salesman Problem using Branch and Bound Method

Subset Sum Problem - Backtracking

Backtracking - HAMILTONION CIRCUIT PROBLEMS

Backtracking – n Queen problem || Design And Analysis of Algorithm

Lower – Bound Arguments – P, NP NP- Complete and NP Hard Problems.

The Simplex Method || ITERATIVE IMPROVEMENT|| Design And Analysis of Algorithm

Maximum Matching in Bipartite Graphs || ITERATIVE IMPROVEMENT || Design and Analysis of Algorithm

Simplex method || ITERATIVE IMPROVEMENT || Design and analysis of Algorithm

Huffman Tree -Greedy Technique | Design and analysis of Algorithm

MULTI STAGE GRAPH || Dynamic programming

Knapsack Problem using Greedy Technique

Container loading problem || Greedy Technique

kruskal's Algorithm|| Greedy Technique Programming || Design and Analysis of Algorithm

Dijkstra’s algorithm|| Greedy Technique || Design and Analysis of Algorithm

Prims Algorithm || Greedy Technique Programming- Design and Analysis of Algorithm

Coin changing problem using Dynamic programming|| Design and Analysis of Algorithm

Divide and conquer - Multiplication of Large Integers

Dynamic programming – Principle of optimality

Divide and conquer methodology merge sort

knapsack problem brute force

Brute Force Assignment problem