Have you ever written an AI? Isn’t it annoying that you have to thread pruning information (e.g. maximum depth, the minimum cost of an adjacent branch, or other such information) through the tree traversal function? This means you have to write a new tree traversal every time you want to improve your AI. That’s dumb. … Read more
Your question seems to be well answered here but to summarise in relation to your specific question it might be better to think of it in reverse; “what happens to the BST solution time as the number of nodes goes up”? Essentially, in a BST every time you double the number of nodes you only … Read more
If a problem is NP-hard, under the assumption that P ≠ NP there is no algorithm that is deterministic, exactly correct on all inputs all the time, and efficient on all possible inputs. If you absolutely need all of the above guarantees, then you’re pretty much out of luck. However, if you’re willing to settle … Read more
A method is linear when the time it takes increases linearly with the number of elements involved. For example, a for loop which prints the elements of an array is roughly linear: for x in range(10): print x because if we print range(100) instead of range(10), the time it will take to run it is … Read more
Spec describes this function as linear search. Array.prototype.includes Let O be ? ToObject(this value). Let len be ? ToLength(? Get(O, “length”)). If len is 0, return false. Let n be ? ToInteger(fromIndex). (If fromIndex is undefined, this step produces the value 0.) If n ≥ 0, then Let k be n. Else n < 0, … Read more
Well, your intuition was right that we need extra data structure to achieve O(klogk) because if we simply perform operations on the original heap, the term logn will remain in the resulting complexity. Guessing from the targeted complexity O(klogk), I feel like creating and maintaining a heap of size k to help me achieve the … Read more
The source is often helpful: http://kickjava.com/src/java/util/HashMap.java.htm remove: O(1) size: O(1) values: O(n) (on traversal through iterator)
In Python, the average time complexity of a dictionary key lookup is O(1), since they are implemented as hash tables. The time complexity of lookup in a list is O(n) on average. In your code, this makes a difference in the line if tmp not in num:, since in the list case, Python needs to … Read more
Hash tables, on average, do have excellent time complexity for insertion, retrieval, and deletion. BUT: Big-O complexity isn’t everything. The constant factor is also very important. You could use hashtables in place of arrays, with the array indexes as hash keys. In either case, the time complexity of retrieving an item is O(1). But the … Read more
I’m surprised that you saw chained hashing to be faster than linear probing – in practice, linear probing is typically significantly faster than chaining. This is primarily due to locality of reference, since the accesses performed in linear probing tend to be closer in memory than the accesses performed in chained hashing. There are other … Read more