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Algorithm of Space Complexity - (BCA / B.Tech)

Algorithm of Space Complexity in Data Structures:

The algorithm for space complexity is used to measure the memory required by any program or algorithm. When analyzing it, we focus on how much memory will be needed while the algorithm is running. Here, we will understand a general algorithm for space complexity.

Algorithm for Space Complexity:

Input Size (n): First, we need to determine the size of the input (n).
Fixed Memory: In this step, we look at all the fixed variables, fixed data structures, and fixed-size memory requirements that will be used in our algorithm. This is often expressed as O(1).
Dynamic Memory: If our algorithm uses any kind of dynamic memory, like a linked list or tree, we must include its space complexity. This usually grows based on the input size (n), such as O(n) or O(n^2).
Function Calls: If the algorithm has many function calls, we must also include the memory required for those.
Total Memory: Finally, we sum the fixed and dynamic memory to get the total space complexity.

Example of Space Complexity Analysis:

Let's say we are creating an algorithm that takes an array as input and calculates the sum of its elements. The space complexity analysis would be:

`int sumArray(int arr[], int size)`

Fixed Memory: O(1) space for the `sum` variable.

Dynamic/Variable Memory: O(n) space for the input array `arr`, which depends on the `size` (n).

Total Space Complexity: Fixed Memory + Dynamic Memory = O(1) + O(n) = O(n).

Algorithm of Space Complexity

Algorithm of Space Complexity in Data Structures:

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