Time Complexity分析:Binary Heap Insert: O(logN), Delete: O(logN), Search: O(N), Space: O(N), Build本来应该O(NlogN), 但是如果用巧妙办法:The optimal method starts by arbitrarily putting the elements on a binary tree, respecting the shape property (the tree could be represented by an ar...
Build本来应该O(NlogN), 但是如果用巧妙办法:The optimal method starts by arbitrarily putting the elements on a binary tree, respecting the shape property (the tree could be represented by an array, see below). Then starting from the lowest level and moving upwards, shift the root of each subt...
This method removes the smallest element from the heap and returns it. It has a time complexity of O(log n) and a space complexity of O(1). heap = [1, 2, 3, 4, 7, 9, 10, 14, 8, 16] heap.heap_pop # => 1 Contributing Bug reports and pull requests are welcome on GitHub ...
N sorting method in my experience, despite that its worst case is quadratic. So I've always qualified O() claims with the intended measure, unless min, max and mean are the same. For example, finding the smallest element in a list is O(n) regardless. Since just finding a minimum is ...