Designing efficient algorithms involves structured approaches to problem-solving. Here are some commonly used algorithm design paradigms: Divide and Conquer Breaks a problem into smaller subproblems, solves them recursively, and combines their results. Example: Merge Sort, Quick Sort, Binary Search Dynamic Programming Solves problems by breaking them into overlapping subproblems and storing results to avoid redundant computations. Example: Fibonacci Series, Knapsack Problem, Longest Common Subsequence Greedy Algorithms Makes locally optimal choices at each step with the hope of finding the global optimum. Example: Huffman Coding, Kruskal’s Algorithm, Prim’s Algorithm Backtracking Explores all possibilities recursively and backtracks when an infeasible solution is found. Example: N-Queens Problem, Sudoku Solver, Hamiltonian Cycle Brute Force Tries all possible solutions and picks the best one. Example: String Matching Algorithms like Naïve Pattern Searching