Apr 6, 2017 · A O(1) function doesn't need to use a fixed size for all inputs, it just has to have a constant upper bound (on space) for all inputs. For example, suppose you have a function that takes a single integer input n, and it uses 10 kB for even n and 20 kB for odd n. This function takes O(1) space, but it certainly doesn't use a fixed size.

Jun 11, 2017 · The assumption underlying concluding that indexing into an array is O(1) is that of random access memory; that we can access location N by encoding N on the address lines of the memory bus, and the contents of that location come back on the data bus.

May 14, 2016 · I have often seen in CLRS texts that the authors use O(1) for a constant statement instead of Theta(1). Well the book is very specific though. One reason for using big-oh for a statement requiring constant time to execute is, people are more concerned about worst case scenario which is reflected by the big-oh notation...

Oct 20, 2009 · A simple example of O(1) might be return 23;-- whatever the input, this will return in a fixed, finite time. A typical example of O(N log N) would be sorting an input array with a good algorithm (e.g. mergesort).

Honestly you would be hard-pressed in any modern language to avoid O(1) extra space for almost any trivial action you could take. The stack counts when giving bounds on algorithms' space complexity. O(1) means constant. Counting sort uses at minimum O(k) space, where k is the largest possible key magnitude.

May 9, 2016 · Hence you access that element as well as any other element of an array in O(1) time. And that is the reason why you cannot have elements of multiple types in an array because that would completely mess up the math to find the address to n th element of an array. So, access to any element in an array is O(1) and all elements have to be of same type.

Mar 2, 2011 · O(1) tells you it doesn't matter how much your input grows, the algorithm will always be just as fast. O(logn) says that the algorithm will be fast, but as your input grows it will take a little longer. O(1) and O(logn) makes a big diference when you start to combine algorithms. Take doing joins with indexes for example.

Mar 21, 2012 · Is there any data structure or variation of existing data structure that offers O(1) or constant time complexity for delete operation ? I know hash table can do it. But I am modifying hash table where, we can get all the the keys without going through all the buckets and in order to do so I am storing every key in another linked list and at the ...

Mar 28, 2015 · There are two settings under which you can get O(1) worst-case times. If your setup is static, then FKS hashing will get you worst-case O(1) guarantees. But as you indicated, your setting isn't static. If you use Cuckoo hashing, then queries and deletes are O(1) worst-case, but insertion is only O(1) expected. Cuckoo hashing works quite well if ...

Aug 31, 2017 · This is a O(1) operation. Appending only is expensive if you overflow available space. Then you have to allocate a larger region, move the whole array, and delete the previous. This is a O(n) operation. But if we're only doing it every O(1/n) times, then on average it can still come out to O(n * 1/n) = O(1).