The time and space complexity of one-dimensional and two-dimensional array operations can vary depending on the specific operation. Here, we’ll discuss common array operations and provide insights into their time and space complexities for one-dimensional and two-dimensional arrays.

One-Dimensional Array Operations:

1. Accessing an Element by Index:
   • Time Complexity: O(1)
   • Space Complexity: O(1)
   • Accessing an element in a one-dimensional array by its index is typically a constant-time operation because it directly computes the memory location of the element.
2. Inserting an Element at the End:
   • Time Complexity: O(1) (Amortized)
   • Space Complexity: O(1)
   • Inserting an element at the end of a one-dimensional array usually involves updating the array’s size and placing the new element in the next available position, which is a constant-time operation on average.
3. Inserting an Element at the Beginning:
   • Time Complexity: O(n)
   • Space Complexity: O(n)
   • Inserting an element at the beginning of a one-dimensional array requires shifting all existing elements to make room, resulting in a linear time and space complexity.
4. Searching for an Element (Linear Search):
   • Time Complexity: O(n)
   • Space Complexity: O(1)
   • In the worst case, searching for an element in a one-dimensional array may require looking at every element, resulting in a linear time complexity. The space complexity remains constant.
5. Deleting an Element:
   • Time Complexity: O(n)
   • space complexity: O(1)
   • In the worst case, deleting an element from the front may take O(n) time as elements after an element should be shifted by one position.

Two-Dimensional Array Operations:

1. Accessing an Element by Indices:
   • Time Complexity: O(1)
   • Space Complexity: O(1)
   • Accessing an element in a two-dimensional array using row and column indices is generally a constant-time operation, similar to one-dimensional arrays.
2. Inserting an Element at a Specific Position:
   • Time Complexity: O(1)
   • Space Complexity: O(1)
   • Inserting an element at a specific position in a two-dimensional array typically has a constant-time complexity because it directly computes the memory location of the element based on its indices.
3. Searching for an Element (Linear Search):
   • Time Complexity: O(m * n)
   • Space Complexity: O(1)
   • When searching for an element in a two-dimensional array, you may need to examine all elements in the worst case, resulting in a time complexity of O(m * n), where ‘m’ is the number of rows and ‘n’ is the number of columns. The space complexity remains constant.
4. Deleting an Element:
   • Time complexity: O(m*n)
   • Space complexity: O(1)
   • Deleting an element from a 2D array requires shifting of elements after deletion operation.
5. Transposing a Matrix:
   • Time Complexity: O(m * n)
   • Space Complexity: O(m * n)
   • Transposing a two-dimensional array involves swapping elements across the diagonal. This operation requires examining and potentially swapping all elements, resulting in a time complexity of O(m * n) and a space complexity of O(m * n).

Below is a table summarizing the time and space complexities of various operations on one-dimensional and two-dimensional arrays.