The Key Differences Between Time and Space Complexity in Linear Data Structures

When we look at linear data structures like arrays and linked lists, it's important to understand two ideas: time complexity and space complexity.

Time Complexity:

• This tells us how long an algorithm takes to finish its job.
• Here are some common types of time complexity:
  • O(1): This means constant time. It takes the same time to complete, no matter how big the input is. For example, just finding an item in an array.
  • O(n): This means linear time. The time it takes depends on the size of the input. For example, checking each item in an array one by one.

Space Complexity:

• This shows how much memory an algorithm needs to run.
• We consider both the space for the input data and any extra space used while the program runs.
• Here are a couple of examples:
  • O(1): This means the algorithm uses a fixed amount of space no matter how big the input is.
  • O(n): This means the algorithm needs more space as it stores more elements, like when it makes a copy of a linked list.

In short, time complexity is about how fast an algorithm runs, while space complexity is about how much memory it uses.