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Plain English explanation of Big O
Many times when time complexity of a algorithm is talked about, memory also comes into account. I want to know what is the meaning of big-O(1), big-O(n), big-O(n*n) memory?
And how it is related to time complexity?
Complexity in terms of mamory mean how fast required memory size growth whilst growing a number of items to be processed. A good example is sorting algorithm.
O(1) and O(log n) mean that whilst sorting N items algorithm requires less memory then total memory allocated for N items. (AKA in-place sorting)O(n) - memory consumption is linear so memory consumption growth as long as items count growthO(n*n) mean that algorithm requires much more additional memory.