Time complexity is a way to evaluate different algorithm performance. This is a relative method, which evaluates the order of input numbers ref. to the steps to be consumed. So the best performance is independent of input numbers, i.e, n power of zero; The worst case, the performance is a result of n square.
Types of Time Complexity:
the worst case, the best case, and average case.
Assumption:
We have infinite memory.
No matter what kind of operations, we assume it takes one unit of time.
List implementations:
get add contains next remove(0) iterator.remove
ArrayList O(1) O(1) O(n) O(1) O(n) O(n)
LinkedList O(n) O(1) O(n) O(1) O(1) O(1)
CopyOnWrite-ArrayList O(1) O(n) O(n) O(1) O(n) O(n)
Set implementations:
add contains next notes
HashSet O(1) O(1) O(h/n) h is the table capacity
LinkedHashSet O(1) O(1) O(1)
CopyOnWriteArraySet O(n) O(n) O(1)
EnumSet O(1) O(1) O(1)
TreeSet O(log n) O(log n) O(log n)
ConcurrentSkipListSet O(log n) O(log n) O(1)
Map implementations:
get containsKey next Notes
HashMap O(1) O(1) O(h/n) h is the table capacity
LinkedHashMap O(1) O(1) O(1)
IdentityHashMap O(1) O(1) O(h/n) h is the table
EnumMap O(1) O(1) O(1)
TreeMap O(log n) O(log n) O(log n)
ConcurrentHashMap O(1) O(1) O(h/n) h is the table
ConcurrentSkipListMap O(log n) O(log n) O(1)
Queue implementations:
offer peek poll size
PriorityQueue O(log n) O(1) O(log n) O(1)
ConcurrentLinkedQueue O(1) O(1) O(1) O(n)
ArrayBlockingQueue O(1) O(1) O(1) O(1)
LinkedBlockingQueue O(1) O(1) O(1) O(1)
PriorityBlockingQueue O(log n) O(1) O(log n) O(1)
DelayQueue O(log n) O(1) O(log n) O(1)
LinkedList O(1) O(1) O(1) O(1)
ArrayDeque O(1) O(1) O(1) O(1)
LinkedBlockingDeque O(1) O(1) O(1) O(1)
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