If one binary tree has x nodes and the other has y nodes where x is bigger than y. I was thinking O(n2) because searching for each node is O(n).
And how about inserting then comparing the trees?
Assuming your binary trees are sorted, this is an O(n) operation (where n is the sum of the nodes in both trees, not the product).
You can simply run two "indexes" side by side through the trees stopping when an element is different. If you get to the end of both and no differences were found, then the trees were identical, something like the following pseudo-code:
def areEqual (tree1, tree2):
pos1 = first (tree1)
pos2 = first (tree2)
while pos1 != END and pos2 != END:
if tree1[pos1] != tree2[pos2]:
return false
pos1 = next (tree1, pos1)
pos2 = next (tree2, pos2)
if pos1 != END or pos2 != END:
return false
return true
If they're not sorted, and you have no other information that may allow you to optimise the function, and cannot use extra data structures, it will be O(n2), since you'll have to find an arbitrary equal node in the second tree for every single node in the first (as well as mark it somehow to indicate you've used it).
Keep in mind there are usually ways to trade space for time if the former is more important (and it often is).
For example, even with totally unordered trees, you can reduce the complexity considerably by using hashing for example:
def areEqual (tree1, tree2):
hash = []
# Add all items from first tree.
for item in tree1.allItems():
if not exists hash[item]
hash[item] = 0
hash[item] += 1
# Subtract all items from second tree.
for item in tree2.allItems():
if not exists hash[item]
hash[item] = 0
hash[item] -= 1
if hash[item] == 0:
delete hash[item]
if hash.size != 0:
return false
return true
Since hashing tends to amortise toward O(1), the problem as a whole can be considered O(n).
Related
I was confronted not so long ago to an algorithmic problem.
I needed to find if a value stored in an array was at it "place".
An example will be easier to understand.
Let's take an Array A = {-10, -3, 3, 5, 7}. The algorithm would return 3, because the number 3 is at A[2] (3rd place).
On the contrary, if we take an Array B = {5, 7, 9, 10}, the algorithm will return 0 or false or whatever.
The array is always sorted !
I wasn't able to find a solution with a good complexity. (Looking at each value individualy is not good !) Maybe it is possible to resolve that problem by using an approach similar to merge sorting, by cuting in half and verifying on those halves ?
Can somebody help me on this one ?
Java algorithm would be the best, but pseudocode would also help me a lot !
Here is an algorithm (based on binary search) to find all matching indices that has a best-case complexity of O(log(n)) and a worst case complexity of O(n):
1- Check the element at position m = array.length / 2
2- if the value array[m] is strictly smaller than m, you can forget about the left half of the array (from index 0 to index m-1), and apply recursively to the right half.
3- if array[m]==m, add one to the counter and apply recursively to both halves
4- if array[m]>m, forget about the right half of the array and apply recursively to the left half.
Using threads can accelerate things here. I suppose that there is no repetitions in the array.
Since there can be no duplicates, you can use the fact that the function f(x): A[x] - x is monotonous and apply binary search to solve the problem in O(log n) worst-case complexity.
You want to find a point where that function A[x] - x takes value zero. This code should work:
boolean binarySearch(int[] data, int size)
{
int low = 0;
int high = size - 1;
while(high >= low) {
int middle = (low + high) / 2;
if(data[middle] - 1 == middle) {
return true;
}
if(data[middle] - 1 < middle) {
low = middle + 1;
}
if(data[middle] - 1 > middle) {
high = middle - 1;
}
}
return false;
}
Watch out for the fact that arrays in Java are 0-indexed - that is the reason why I subtract -1 from the array.
If you want the find the first number in the array that is at its own place, you just have to iterate the array:
static int find_in_place(int[] a) {
for (int i=0; i<a.length; i++) {
if (a[i] == i+1) {
return a[i];
}
}
return 0;
}
It has a complexity of O(n), and an average cost of n/2
You can skip iterating if there is no such element by adding a special condition
if(a[0]>1 && a[a.length-1]>a.length){
//then don't iterate through the array and return false
return false;
} else {
//make a loop here
}
Using binary search (or a similar algorithm) you could get better than O(n). Since the array is sorted, we can make the following assumptions:
if the value at index x is smaller than x-1 (a[x] <= x), you know that all previous values also must be smaller than their index (because no duplicates are allowed)
if a[x] > x + 1 all following values must be greater than their index (again no duplicates allowed).
Using that you can use a binary approach and pick the center value, check for its index and discard the left/right part if it matches one of the conditions above. Of course you stop when a[x] = x + 1.
simply use a binary search for the 0 and use for compare the value in the array minus index of the array. O(log n)
Say we have an array
a[] ={1,2,-3,3,-3,-3,4,-4,5}
And find the position of 3 (which would be 3)
There are be no multiple indexes for an answer.
It must be efficient, and NOT linear.
I was thinking of doing a Binary Search of the array, but instead of comparing the raw values, I wanted to compare the absolute values; abs(a[i]) and abs(n) [n is the input number]. Then if the values are equal, I do another comparison, now with the raw values a[i] and n.
But I run into a problem where, if I am in the above situation with the same array {1,2,-3,3,-3,-3,4,-4,5}, and am looking for 3, there are multiple -3 that get in the way (thus, I would have to check if the raw values a[i] and n does not work, I have to check a[i+1] and a[i-1].)
Ok im just rambling now. Am i thinking too hard for this?
Help me out thanks!!! :D
It is a modified binary search problem. The difference between this and regular binary search is that you need to find and test all of the elements that compare as equal according to the sorting criterion.
I would:
use a tweaked binary search algorithm to find the index of the left-most element that matches
iterate through the indexes until you find the element are looking for, or an element whose absolute value no longer matches.
That should be O(logN) for the first step. The second step is O(1) on average if you assume that the element values are evenly distributed. (The worst case for the second step is O(N); e.g. when the elements all have the same absolute value, and the one you want is the last in the array.)
Here's the method to solve your problem:
/**
* #param a array sorted by absolute value
* #param key value to find (must be positive)
* #return position of the first occurence of the key or -1 if key not found
*/
public static int binarySearch(int[] a, int key) {
int low = 0;
int high = a.length-1;
while (low <= high) {
int mid = (low + high) >>> 1;
int midVal = Math.abs(a[mid]);
if (midVal < key)
low = mid + 1;
else if (midVal > key || (midVal == key && mid > 0 && Math.abs(a[mid-1]) == key))
high = mid - 1;
else
return mid; // key found
}
return -1; // key not found.
}
It's a modification of Arrays.binarySearch from JDK. There are several changes. First, we compare absolute values. Second, as you want not any key position, but the first one, I modified a condition: if we found a key we check whether the previous array item has the same value. If yes, then we continue search. This way algorithm remains O(log N) even for special cases where too many values which are equal to key.
I have been trying to get this to work but while it works for majority of the input sometimes it gives the wrong output. I have spent some time debugging the code and it seems the problem is when i get a Node that is smaller than the root but bigger than the left node under the root.
How can I traverse the right sub-tree and still return the right key if no node in the right sub-tree is the floor node for that key?
Recall that if you do anything recursively, it can be transformed* into iteration.
Let's consider taking the floor of a well-formed BST, which should simply be the smallest element which is less than or equal to your key in the tree. All we have to do is traverse the tree to get it.
Let's implement it recursively so we can tease out a few important corollaries between iteration and recursion.
// Assuming non-null root node with method declaration
private Node floor(Node root, Key key, Node lowestNode) {
if(key.compareTo(root.getKey()) <= 0) {
if(root.getLeft() != null) {
return floor(root.getLeft(), key, lowestNode);
} else {
return root.compareTo(lowestNode) < 0 ? root : lowestNode;
}
} else {
if(root.getRight() != null) {
lowestRightNode.add(root);
return floor(root.getRight(), key, lowestNode);
} else {
return lowestNode;
}
}
Let's walk through the conditions for success.
If we compare a node to be less than or equal to our key value:
If we have a left child, there's something smaller. Traverse down the left half of the tree.
Otherwise, we're at the floor - which means we're at the node whose value is less than or equal to our key. Return it.
Otherwise (our node has a value greater than our key):
If we have a right child, there's a chance that our work isn't done yet (something's smaller). We'd like to keep it around since we could step off of the tree, so let's store it, then traverse down the right half of the tree.
Otherwise, we've fallen off of the tree. Return the smallest element we've kept track of.
An example may look something like this:
9
/ \
3 14
/ \
1 2
With a key of 12:
Compare with 9. We're larger. Store 9 in our lowest node variable, recurse right.
Compare with 14. We're smaller, but we don't have a left child. We compare the value 14 to 9 and 9 is smaller, so we return the node with 9.
If we want to convert this into iteration, then think about your starting point, your conditional check, and your incrementation steps.
Starting point: A non-null node
Conditional check:
key.compareTo(root.getKey()) <= 0
root.getLeft() != null
continue
root.compareTo(lowestRightNode) < 0 ? root : lowestRightNode
terminal
else
root.getRight() != null
store temp value and continue
return lowestRightNode
terminal
Pay close attention to your continuation conditions, and what other work you'd have to do to keep track of the lowest node you've seen so far (only for the right-hand side, that is).
*: Some recursive operations are more painful to convert than others, of course.
i have implemented a function to find the depth of a node in a binary search tree but my implementation does not take care of duplicates. I have my code below and would like some suggestions on how to consider duplicates case in this function. WOuld really appreciate your help.
public int depth(Node n) {
int result=0;
if(n == null || n == getRoot())
return 0;
return (result = depth(getRoot(), n, result));
}
public int depth(Node temp, Node n, int result) {
int cmp = n.getData().compareTo(temp.getData());
if(cmp == 0) {
int x = result;
return x;
}
else if(cmp < 0) {
return depth(temp.getLeftChild(), n, ++result);
}
else {
return depth(temp.getRightChild(), n, ++result);
}
}
In the code you show, there is no way to prefer one node with same value over another. You need to have some criteria for differentiation.
You can retrieve the list of all duplicate nodes depths using the following approach, for example:
Find the depth of your node.
Find depth of the same node for the left subtree emerging from the found node - stop if not found.
Add depth of the previously found node (in 1) to the depth of the duplicate
Find depth of the same node for the right subtree emerging from the found node (in 1) - stop if not found.
Add depth of the previously found node (in 1) to the depth of the duplicate
Repeat for left and right subtrees.
Also see here: What's the case for duplications in BST?
Well, if there's duplicates, then the depth of a node with a given value doesn't make any sense on its own, because there may be multiple nodes with that value, hence multiple depths.
You have to decide what it means, which could be (not necessarily an exhaustive list):
the depth of the deepest node with that value.
the depth of the shallowest node with that value.
the depth of the first node found with that value.
the average depth of all nodes with that value.
the range (min/max) of depths of all nodes with that value.
a list of depths of all nodes with that value.
an error code indicating your query made little sense.
Any of those could make sense in specific circumstances.
Of course, if n is an actual pointer to a node, you shouldn't be comparing values of nodes at all, you should be comparing pointers. That way, you will only ever find one match and the depth of it makes sense.
Something like the following pseudo-code should do:
def getDepth (Node needle, Node haystack, int value):
// Gone beyond leaf, it's not in tree
if haystack == NULL: return -1
// Pointers equal, you've found it.
if needle == haystack: return value
// Data not equal search either left or right subtree.
if needle.data < haystack.data:
return getDepth (needle, haystack.left, value + 1)
if needle.data > haystack.data:
return getDepth (needle, haystack.right, value + 1)
// Data equal, need to search BOTH subtrees.
tryDepth = getDepth (needle, haystack.left, value + 1)
if trydepth == -1:
tryDepth = getDepth (needle, haystack.right, value + 1)
return trydepth
The reason why you have to search both subtrees when the values are equal is because the desired node may be in either subtree. Where the values are unequal, you know which subtree it's in. So, for the case where they're equal, you check one subtree and, if not found, you check the other.
If you are provided the head of a linked list, and are asked to reverse every k sequence of nodes, how might this be done in Java? e.g., a->b->c->d->e->f->g->h with k = 3 would be c->b->a->f->e->d->h->g->f
Any general help or even pseudocode would be greatly appreciated! Thanks!
If k is expected to be reasonably small, I would just go for the simplest thing: ignore the fact that it's a linked list at all, and treat each subsequence as just an array-type thing of things to be reversed.
So, if your linked list's node class is a Node<T>, create a Node<?>[] of size k. For each segment, load k Nodes into the array list, then just reverse their elements with a simple for loop. In pseudocode:
// reverse the elements within the k nodes
for i from 0 to k/2:
nodeI = segment[i]
nodeE = segment[segment.length-i-1]
tmp = nodeI.elem
nodeI.elem = nodeE.elem
nodeE.elem = tmp
Pros: very simple, O(N) performance, takes advantage of an easily recognizable reversing algorithm.
Cons: requires a k-sized array (just once, since you can reuse it per segment)
Also note that this means that each Node doesn't move in the list, only the objects the Node holds. This means that each Node will end up holding a different item than it held before. This could be fine or not, depending on your needs.
This is pretty high-level, but I think it'll give some guidance.
I'd have a helper method like void swap3(Node first, Node last) that take three elements at an arbitrary position of the list and reverses them. This shouldn't be hard, and could could be done recursively (swap the outer elements, recurse on the inner elements until the size of the list is 0 or 1). Now that I think of it, you could generalize this into swapK() easily if you're using recursion.
Once that is done, then you can just walk along your linked list and call swapK() every k nodes. If the size of the list isn't divisble by k, you could either just not swap that last bit, or reverse the last length%k nodes using your swapping technique.
TIME O(n); SPACE O(1)
A usual requirement of list reversal is that you do it in O(n) time and O(1) space. This eliminates recursion or stack or temporary array (what if K==n?), etc.
Hence the challenge here is to modify an in-place reversal algorithm to account for the K factor. Instead of K I use dist for distance.
Here is a simple in-place reversal algorithm: Use three pointers to walk the list in place: b to point to the head of the new list; c to point to the moving head of the unprocessed list; a to facilitate swapping between b and c.
A->B->C->D->E->F->G->H->I->J->L //original
A<-B<-C<-D E->F->G->H->I->J->L //during processing
^ ^
| |
b c
`a` is the variable that allow us to move `b` and `c` without losing either of
the lists.
Node simpleReverse(Node n){//n is head
if(null == n || null == n.next)
return n;
Node a=n, b=a.next, c=b.next;
a.next=null; b.next=a;
while(null != c){
a=c;
c=c.next;
a.next=b;
b=a;
}
return b;
}
To convert the simpleReverse algorithm to a chunkReverse algorithm, do following:
1] After reversing the first chunk, set head to b; head is the permanent head of the resulting list.
2] for all the other chunks, set tail.next to b; recall that b is the head of the chunk just processed.
some other details:
3] If the list has one or fewer nodes or the dist is 1 or less, then return the list without processing.
4] use a counter cnt to track when dist consecutive nodes have been reversed.
5] use variable tail to track the tail of the chunk just processed and tmp to track the tail of the chunk being processed.
6] notice that before a chunk is processed, it's head, which is bound to become its tail, is the first node you encounter: so, set it to tmp, which is a temporary tail.
public Node reverse(Node n, int dist) {
if(dist<=1 || null == n || null == n.right)
return n;
Node tail=n, head=null, tmp=null;
while(true) {
Node a=n, b=a.right; n=b.right;
a.right=null; b.right=a;
int cnt=2;
while(null != n && cnt < dist) {
a=n; n=n.right; a.right=b; b=a;
cnt++;
}
if(null == head) head = b;
else {
tail.right=b;tail=tmp;
}
tmp=n;
if(null == n) return head;
if(null == n.right) {
tail.right=n;
return head;
}
}//true
}
E.g. by Common Lisp
(defun rev-k (k sq)
(if (<= (length sq) k)
(reverse sq)
(concatenate 'list (reverse (subseq sq 0 k)) (rev-k k (subseq sq k)))))
other way
E.g. by F# use Stack
open System.Collections.Generic
let rev_k k (list:'T list) =
seq {
let stack = new Stack<'T>()
for x in list do
stack.Push(x)
if stack.Count = k then
while stack.Count > 0 do
yield stack.Pop()
while stack.Count > 0 do
yield stack.Pop()
}
|> Seq.toList
Use a stack and recursively remove k items from the list, push them to the stack then pop them and add them in place. Not sure if it's the best solution, but stacks offer a proper way of inverting things. Notice that this also works if instead of a list you had a queue.
Simply dequeue k items, push them to the stack, pop them from the stack and enqueue them :)
This implementation uses ListIterator class:
LinkedList<T> list;
//Inside the method after the method's parameters check
ListIterator<T> it = (ListIterator<T>) list.iterator();
ListIterator<T> reverseIt = (ListIterator<T>) list.listIterator(k);
for(int i = 0; i< (int) k/2; i++ )
{
T element = it.next();
it.set(reverseIt.previous());
reverseIt.set(element);
}