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 To evaluate the efficiency of an algorithm, we analyze its performance using the following measures: 1. Time Complexity Represents the time taken by an algorithm to run as a function of input size (n). Expressed using Big-O notation (O). Examples: O(1) - Constant time O(log n) - Logarithmic time (Binary Search) O(n) - Linear time (Linear Search) O(n log n) - Log-linear time (Merge Sort) O(n²) - Quadratic time (Bubble Sort) 2. Space Complexity Represents the memory required by an algorithm. Important for optimizing performance in memory-constrained environments. 3. Best, Average, and Worst Case Analysis Best Case : Minimum time required (ideal scenario) Average Case : Expected performance over different inputs Worst Case : Maximum time required (upper bound)

 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