Time-space tradeoff is a concept in computer science that refers to the relationship between the amount of time a computer algorithm takes to execute and the amount of space (memory) it requires. In other words, it is the tradeoff between the speed of an algorithm and the amount of resources it consumes.

For example, consider a computer algorithm that sorts a large list of numbers. One way to sort the list is to use a quick sort algorithm, which has a time complexity of O(nlogn). This means that the algorithm’s execution time increases at a rate that is logarithmic with respect to the size of the input. However, the quick sort algorithm requires a significant amount of space (memory) to store the temporary arrays and variables needed for the sorting process.

On the other hand, another algorithm for sorting the list is the merge sort algorithm, which has a time complexity of O(nlog(n)). This means that it takes longer to execute compared to the quick sort algorithm. However, the merge sort algorithm requires less space (memory) as it does not need to store any temporary arrays or variables.

In this example, the quick sort algorithm has a better time complexity, but it requires more space (memory). On the other hand, the merge sort algorithm has a worse time complexity, but it requires less space (memory). This is an example of time-space tradeoff, where a tradeoff must be made between the speed of an algorithm and the amount of resources it consumes.