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Given an array of integers, find the first missing positive integer in linear time and constant space. In other words, find the lowest positive integer that does not exist in the array. The array can contain duplicates and negative numbers as well.
For example, the input [3, 4, -1, 1] should give 2. The input [1, 2, 0] should give 3.
I did this but could not get through it and then searched it on google and got an answer on geeks for geeks but could not understand it. Can anyone provide a logic for this using simple concepts? I have just started competitive programming.
One way to find the solution is to rearrange the array, and then finding the first
number misplaced:
int find_missing(std::vector<int>& v)
{
for (std::size_t i = 0; i != v.size(); ++i) {
std::size_t e = i;
while (0 < v[e] // Correct range
&& std::size_t(v[e]) <= v.size() // Correct range
&& std::size_t(v[e]) != e + 1 // Correct place
&& v[e] != v[v[e] - 1] // Duplicate
) {
std::swap(v[e], v[v[e] - 1]);
}
}
// Now the array look like
// {1, 2, 3, x, 5, 6, x}
// Find first misplaced number
for (std::size_t i = 0; i != v.size(); ++i) {
if (std::size_t(v[i]) != i + 1) {
return i + 1;
}
}
// All are correctly placed:
return v.size();
}
Demo
If a bitmap (extension of bitmask) is acceptable, then we could use 1 bit per positive integer and then just scroll the array. The bitmap is initialized with all bits to 0. As we scroll the array, we ignore negatives and turn the nth bit on when we encounter n. When we find, for example, 13, we turn the 13th bit into 1. (Likewise the number 1 would turn the first bit to 1) Then we scroll the bitmask and check the first zero. Done.
However, this might not be considered a constant complexity at all, since when the max positive int is MAXINT, we need the bitmap to be MAXINT bits large. Too bad. In theory, though, this is correct. Also O(2*N) = O(N)
So we have to store some information in the array or this is impossible to solve in O(N) in a single go.
Another solution consists in mapping array index with integer and storing information using sign. If the array size is L, for example, the missing int will be less or equal to L+1 (L+1 when the array if full like [1,2,3,4], unless this case counts as no element missing). Thanks Jarod for the hint on this.
Considered O(3N) is still O(N), how about:
step 1: scroll the array and swap negatives and zeroes moving them to the beginning. Turn everything non positive, that was swapped this way, to 1. The authentic positives will start at index j.
step 2: The whole array is now positive but true data lies from j to the end of the array. Scroll the subarray with authentic data and when you find, say, number H, turn index the Hth indexed number of the whole array negative. If H is greater than the array size, skip it. When you find for example 2, turn arr[1] (second element) negative.
step 3: scroll again the array checking for the first positive number. Basing on the index you know what the first missing positive integer is.
I am looking for an algorithm to solve the following problem: We are given an integer array of size n which contains k (0 < k < n) many elements exactly once. Every other integer occurs an even number of times in the array. The output should be any of the k unique numbers. k is a fixed number and not part of the input.
An example would be the input [1, 2, 2, 4, 4, 2, 2, 3] with both 1 and 3 being a correct output.
Most importantly, the algorithm should run in O(n) time and require only O(1) additional space.
edit: There has been some confusion regarding whether there is only one unique integer or multiple. I apologize for this. The correct problem is that there is an arbitrary but fixed amount. I have updated the original question above.
"Dante." gave a good answer for the case that there are at most two such numbers. This link also provides a solution for three. "David Eisenstat" commented that it is also possible to do for any fixed k. I would be grateful for a solution.
There is a standard algorithm to solve such problems using XOR operator:
Time Complexity = O(n)
Space Complexity = O(1)
Suppose your input array contains only one element that occurs odd no of times and rest occur even number of times,we take advantage of the following fact:
Any expression having even number of 0's and 1's in any order will always be = 0 when xor is applied.
That is
0^1^....... = 0 as long as number of 0 is even and number of 1 is even
and 0 and 1 can occur in any order.
Because all numbers that occur even number of times will have their corresponding bits form even number of 1's and 0's and only the number which occurs only once will have its bit left out when we take xor of all elements of array because
0(from no's occuring even times)^1(from no occuring once) = 1
0(from no's occuring even times)^0(from no occuring once) = 0
as you can see the bit of only the number occuring once is preserved.
This means when given such an array and you take xor of all the elements,the result is the number which occurs only once.
So the algorithm for array of length n is:
result = array[0]^array[1]^.....array[n-1]
Different Scenario
As the OP mentioned that input can also be an array which has two numbers occuring only once and rest occur even number of times.
This is solved using the same logic as above but with little difference.
Idea of algorithm:
If you take xor of all the elements then definitely all the bits of elements occuring even number of times will result in 0,which means:
The result will have its bit 1 only at that bit position where the bits of the two numbers occuring only once differ.
We will use the above idea.
Now we focus on the resultant xor bit which is 1(any bit which is 1) and make rest 0.The result is a number which will allow us to differentiate between the two numbers(the required ones).
Because the bit is 1,it means they differ at this position,it means one will have 0 at this position and one will have 1.This means one number when taken AND results in 0 and one does not.
Since it is very easy to set the right most bit,we set it of the result xor as
A = result & ~(result-1)
Now traverse through the array once and if array[i]&A is 0 store the number in variable number_1 as
number_1 = number_1^array[i]
otherwise
number_2 = number_2^array[i]
Because the remaining numbers occur even number of times,their bit will automatically disappear.
So the algorithm is
1.Take xor of all elements,call it xor.
2.Set the rightmost bit of xor and store it in B.
3.Do the following:
number_1=0,number_2=0;
for(i = 0 to n-1)
{
if(array[i] & B)
number_1 = number_1^array[i];
else
number_2 = number_2^array[i];
}
The number_1 and number_2 are the required numbers.
Here's a Las Vegas algorithm that, given k, the exact number of elements that occur an odd number of times, reports all of them in expected time O(n k) (read: linear-time when k is O(1)) and space O(1) words, assuming that "give me a uniform random word" and "give me the number of 1 bits set in this word (popcount)" are constant-time operations. I'm pretty sure that I'm not the first person to come up with this algorithm (and I'm not even sure that I'm remembering all of the refinements), but I've reached the limits of my patience trying to find it.
The central technique is called random restrictions. Essentially what we do is to filter the input randomly by value, in the hope that we retain exactly one odd-count element. We apply the classic XOR algorithm to the filtered array and check the result; if it succeeded, then we pretend to add it to the array, to make it even-count. Repeat until all k elements are found.
The filtration process goes like this. Treat each input word x as a binary vector of length w (doesn't matter what w is). Compute a random binary matrix A of size w by ceil(1 + lg k) and a random binary vector b of length ceil(1 + lg k). We filter the input by retaining those x such that Ax = b, where the left-hand side is a matrix multiplication mod 2. In implementation, A is represented as ceil(1 + lg k) vectors a1, a2, .... We compute the bits of Ax as popcount(a1 ^ x), popcount(a2 ^ x), .... (This is convenient because we can short-circuit the comparison with b, which shaves a factor lg k from the running time.)
The analysis is to show that, in a given pass, we manage with constant probability to single out one of the odd-count elements. First note that, for some fixed x, the probability that Ax = b is 2-ceil(1 + lg k) = Θ(1/k). Given that Ax = b, for all y ≠ x, the probability that Ay = b is less than 2-ceil(1 + lg k). Thus, the expected number of elements that accompany x is less than 1/2, so with probability more than 1/2, x is unique in the filtered input. Sum over all k odd-count elements (these events are disjoint), and the probability is Θ(1).
Here's a deterministic linear-time algorithm for k = 3. Let the odd-count elements be a, b, c. Accumulate the XOR of the array, which is s = a ^ b ^ c. For each bit i, observe that, if a[i] == b[i] == c[i], then s[i] == a[i] == b[i] == c[i]. Make another pass through the array, accumulate the XOR of the lowest bit set in s ^ x. The even-count elements contribute nothing again. Two of the odd-count elements contribute the same bit and cancel each other out. Thus, the lowest bit set in the XOR is where exactly one of the odd-count elements differs from s. We can use the restriction method above to find it, then the k = 2 method to find the others.
The question title says "the unique integer", but the question body says there can be more than one unique element.
If there is in fact only one non-duplicate: XOR all the elements together. The duplicates all cancel, because they come in pairs (or higher multiples of 2), so the result is the unique integer.
See Dante's answer for an extension of this idea that can handle two unique elements. It can't be generalized to more than that.
Perhaps for k unique elements, we could use k accumulators to track sum(a[i]**k). i.e. a[i], a[i]2, etc. This probably only works for Faster algorithm to find unique element between two arrays?, not this case where the duplicates are all in one array. IDK if an xor of squares, cubes, etc. would be any use for resolving things.
Track the counts for each element and only return the elements with a count of 1. This can be done with a hash map. The below example tracks the result using a hash set while it's still building the counts map. Still O(n) but less efficient, but I think it's slightly more instructive.
Javascript with jsfiddle http://jsfiddle.net/nmckchsa/
function findUnique(arr) {
var uniq = new Map();
var result = new Set();
// iterate through array
for(var i=0; i<arr.length; i++) {
var v = arr[i];
// add value to map that contains counts
if(uniq.has(v)) {
uniq.set(v, uniq.get(v) + 1);
// count is greater than 1 remove from set
result.delete(v);
} else {
uniq.set(v, 1);
// add a possibly uniq value to the set
result.add(v);
}
}
// set to array O(n)
var a = [], x = 0;
result.forEach(function(v) { a[x++] = v; });
return a;
}
alert(findUnique([1,2,3,0,1,2,3,1,2,3,5,4,4]));
EDIT Since the non-uniq numbers appear an even number of times #PeterCordes suggested a more elegant set toggle.
Here's how that would look.
function findUnique(arr) {
var result = new Set();
// iterate through array
for(var i=0; i<arr.length; i++) {
var v = arr[i];
if(result.has(v)) { // even occurances
result.delete(v);
} else { // odd occurances
result.add(v);
}
}
// set to array O(n)
var a = [], x = 0;
result.forEach(function(v) { a[x++] = v; });
return a;
}
JSFiddle http://jsfiddle.net/hepsyqyw/
Assuming you have an input array: [2,3,4,2,4]
Output: 3
In Ruby, you can do something as simple as this:
[2,3,4,2,4].inject(0) {|xor, v| xor ^ v}
Create an array counts that has INT_MAX slots, with each element initialized to zero.
For each element in the input list, increment counts[element] by one. (edit: actually, you will need to do counts[element] = (counts_element+1)%2, or else you might overflow the value for really ridiculously large values of N. It's acceptable to do this kind of modulus counting because all duplicate items appear an even number of times)
Iterate through counts until you find a slot that contains "1". Return the index of that slot.
Step 2 is O(N) time. Steps 1 and 3 take up a lot of memory and a lot of time, but neither one is proportional to the size of the input list, so they're still technically O(1).
(note: this assumes that integers have a minimum and maximum value, as is the case for many programming languages.)
Given an array find the next smaller element in array for each element without changing the original order of the elements.
For example, suppose the given array is 4,2,1,5,3.
The resultant array would be 2,1,-1,3,-1.
I was asked this question in an interview, but i couldn't think of a solution better than the trivial O(n^2) solution.
Any approach that I could think of, i.e. making a binary search tree, or sorting the array, will distort the original order of the elements and hence lead to a wrong result.
Any help would be highly appreciated.
O(N) Algorithm
Initialize output array to all -1s.
Create an empty stack of indexes of items we have visited in the input array but don't yet know the answer for in the output array.
Iterate over each element in the input array:
Is it smaller than the item indexed by the top of the stack?
Yes. It is the first such element to be so. Fill in the corresponding element in our output array, remove the item from the stack, and try again until the stack is empty or the answer is no.
No. Continue to 3.2.
Add this index to the stack. Continue iteration from 3.
Python implementation
def find_next_smaller_elements(xs):
ys=[-1 for x in xs]
stack=[]
for i,x in enumerate(xs):
while len(stack)>0 and x<xs[stack[-1]]:
ys[stack.pop()]=x
stack.append(i)
return ys
>>> find_next_smaller_elements([4,2,1,5,3])
[2, 1, -1, 3, -1]
>>> find_next_smaller_elements([1,2,3,4,5])
[-1, -1, -1, -1, -1]
>>> find_next_smaller_elements([5,4,3,2,1])
[4, 3, 2, 1, -1]
>>> find_next_smaller_elements([1,3,5,4,2])
[-1, 2, 4, 2, -1]
>>> find_next_smaller_elements([6,4,2])
[4, 2, -1]
Explanation
How it works
This works because whenever we add an item to the stack, we know its value is greater or equal to every element in the stack already. When we visit an element in the array, we know that if it's lower than any item in the stack, it must be lower than the last item in the stack, because the last item must be the largest. So we don't need to do any kind of search on the stack, we can just consider the last item.
Note: You can skip the initialization step so long as you add a final step to empty the stack and use each remaining index to set the corresponding output array element to -1. It's just easier in Python to initialize it to -1s when creating it.
Time complexity
This is O(N). The main loop clearly visits each index once. Each index is added to the stack exactly once and removed at most once.
Solving as an interview question
This kind of question can be pretty intimidating in an interview, but I'd like to point out that (hopefully) an interviewer isn't going to expect the solution to spring from your mind fully-formed. Talk them through your thought process. Mine went something like this:
Is there some relationship between the positions of numbers and their next smaller number in the array? Does knowing some of them constrain what the others might possibly be?
If I were in front of a whiteboard I would probably sketch out the example array and draw lines between the elements. I might also draw them as a 2D bar graph - horizontal axis being position in input array and vertical axis being value.
I had a hunch this would show a pattern, but no paper to hand. I think the diagram would make it obvious. Thinking about it carefully, I could see that the lines would not overlap arbitrarily, but would only nest.
Around this point, it occurred to me that this is incredibly similar to the algorithm Python uses internally to transform indentation into INDENT and DEDENT virtual tokens, which I'd read about before. See "How does the compiler parse the indentation?" on this page: http://www.secnetix.de/olli/Python/block_indentation.hawk However, it wasn't until I actually worked out an algorithm that I followed up on this thought and determined that it was in fact the same, so I don't think it helped too much. Still, if you can see a similarity to some other problem you know, it's probably a good idea to mention it, and say how it's similar and how it's different.
From here the general shape of the stack-based algorithm became apparent, but I still needed to think about it a bit more to be sure it would work okay for those elements that have no subsequent smaller element.
Even if you don't come up with a working algorithm, try to let your interviewer see what you're thinking about. Often it is the thought process more than the answer that they're interested in. For a tough problem, failing to find the best solution but showing insight into the problem can be better than knowing a canned answer but not being able to give it much analysis.
Start making a BST, starting from the array end. For each value 'v' answer would be the last node "Right" that you took on your way to inserting 'v', of which you can easily keep track of in recursive or iterative version.
UPDATE:
Going by your requirements, you can approach this in a linear fashion:
If every next element is smaller than the current element(e.g. 6 5 4 3 2 1) you can process this linearly without requiring any extra memory. Interesting case arises when you start getting jumbled elements(e.g. 4 2 1 5 3), in which case you need to remember their order as long as you dont' get their 'smaller counterparts'.
A simple stack based approach goes like this:
Push the first element (a[0]) in a stack.
For each next element a[i], you peek into the stack and if value ( peek() ) is greater than the one in hand a[i], you got your next smaller number for that stack element (peek()) { and keep on popping the elements as long as peek() > a[i] }. Pop them out and print/store the corresponding value.
else, simply push back your a[i] into the stack.
In the end stack 'll contain those elements which never had a value smaller than them(to their right). You can fill in -1 for them in your outpput.
e.g. A=[4, 2, 1, 5, 3];
stack: 4
a[i] = 2, Pop 4, Push 2 (you got result for 4)
stack: 2
a[i] = 1, Pop 2, Push 1 (you got result for 2)
stack: 1
a[i] = 5
stack: 1 5
a[i] = 3, Pop 5, Push 3 (you got result for 5)
stack: 1 3
1,3 don't have any counterparts for them. so store -1 for them.
Assuming you meant first next element which is lower than the current element, here are 2 solutions -
Use sqrt(N) segmentation. Divide the array in sqrt(N) segments with each segment's length being sqrt(N). For each segment calculate its' minimum element using a loop. In this way, you have pre-calculated each segments' minimum element in O(N). Now, for each element, the next lower element can be in the same segment as that one or in any of the subsequent segments. So, first check all the next elements in the current segment. If all are larger, then loop through all the subsequent segments to find out which has an element lower than current element. If you couldn't find any, result would be -1. Otherwise, check every element of that segment to find out what is the first element lower than current element. Overall, algorithm complexity is O(N*sqrt(N)) or O(N^1.5).
You can achieve O(NlgN) using a segment tree with a similar approach.
Sort the array ascending first (keeping original position of the elements as satellite data). Now, assuming each element of the array is distinct, for each element, we will need to find the lowest original position on the left side of that element. It is a classic RMQ (Range Min Query) problem and can be solved in many ways including a O(N) one. As we need to sort first, overall complexity is O(NlogN). You can learn more about RMQ in a TopCoder tutorial.
For some reasons, I find it easier to reason about "previous smaller element", aka "all nearest smaller elements". Thus applied backward gives the "next smaller".
For the record, a Python implementation in O(n) time, O(1) space (i.e. without stack), supporting negative values in the array :
def next_smaller(l):
""" Return positions of next smaller items """
res = [None] * len(l)
for i in range(len(l)-2,-1,-1):
j=i+1
while j is not None and (l[j] > l[i]):
j = res[j]
res[i] = j
return res
def next_smaller_elements(l):
""" Return next smaller items themselves """
res = next_smaller(l)
return [l[i] if i is not None else None for i in res]
Here is the javascript code . This video explains the Algo better
function findNextSmallerElem(source){
let length = source.length;
let outPut = [...Array(length)].map(() => -1);
let stack = [];
for(let i = 0 ; i < length ; i++){
let stackTopVal = stack[ stack.length - 1] && stack[ stack.length - 1].val;
// If stack is empty or current elem is greater than stack top
if(!stack.length || source[i] > stackTopVal ){
stack.push({ val: source[i], ind: i} );
} else {
// While stacktop is greater than current elem , keep popping
while( source[i] < (stack[ stack.length - 1] && stack[ stack.length - 1].val) ){
outPut[stack.pop().ind] = source[i];
}
stack.push({ val: source[i], ind: i} );
}
}
return outPut;
}
Output -
findNextSmallerElem([98,23,54,12,20,7,27])
[23, 12, 12, 7, 7, -1, -1]
Time complexity O(N), space complexity O(N).
Clean solution on java keeping order of the array:
public static int[] getNGE(int[] a) {
var s = new Stack<Pair<Integer, Integer>>();
int n = a.length;
var result = new int[n];
s.push(Pair.of(0, a[0]));
for (int i = 1; i < n; i++) {
while (!s.isEmpty() && s.peek().v2 > a[i]) {
var top = s.pop();
result[top.v1] = a[i];
}
s.push(Pair.of(i, a[i]));
}
while (!s.isEmpty()) {
var top = s.pop();
result[top.v1] = -1;
}
return result;
}
static class Pair<K, V> {
K v1;
V v2;
public static <K, V> Pair<K, V> of (K v1, V v2) {
Pair p = new Pair();
p.v1 = v1;
p.v2 = v2;
return p;
}
}
Here is an observation that I think can be made into an O(n log n) solution. Suppose you have the answer for the last k elements of the array. What would you need in order to figure out the value for the element just before this? You can think of the last k elements as being split into a series of ranges, each of which starts at some element and continues forward until it hits a smaller element. These ranges must be in descending order, so you could think about doing a binary search over them to find the first interval smaller than that element. You could then update the ranges to factor in this new element.
Now, how best to represent this? The best way I've thought of is to use a splay tree whose keys are the elements defining these ranges and whose values are the index at which they start. You can then in time O(log n) amortized do a predecessor search to find the predecessor of the current element. This finds the earliest value smaller than the current. Then, in amortized O(log n) time, insert the current element into the tree. This represents defining a new range from that element forward. To discard all ranges this supercedes, you then cut the right child of the new node, which because this is a splay tree is at the root, from the tree.
Overall, this does O(n) iterations of an O(log n) process for total O(n lg n).
Here is a O(n) algorithm using DP (actually O(2n) ):
int n = array.length();
The array min[] record the minimum number found from index i until the end of the array.
int[] min = new int[n];
min[n-1] = array[n-1];
for(int i=n-2; i>=0; i--)
min[i] = Math.min(min[i+1],array[i]);
Search and compare through the original array and min[].
int[] result = new int[n];
result[n-1] = -1;
for(int i=0; i<n-1; i++)
result[i] = min[i+1]<array[i]?min[i+1]:-1;
Here is the new solution to find "next smaller element":
int n = array.length();
int[] answer = new int[n];
answer[n-1] = -1;
for(int i=0; i<n-1; i++)
answer[i] = array[i+1]<array[i]?array[i+1]:-1;
All that is actually not required i think
case 1: a,b
answer : -a+b
case 2: a,b,c
answer : a-2b+c
case 3: a,b,c,d
answer : -a+3b-3c+d
case 4 :a,b,c,d,e
answer : a-4b+6c-4d+e
.
.
.
recognize the pattern in it?
it is the pascal's triangle!
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
so it can be calculated using Nth row of pascal's triangle!
with alternate + ans - for odd even levels!
it is O(1)
You can solve this in O(n) runtime with O(n) space complexity.
Start with a Stack and keep pushing elements till you find arr[i] such that arr[i] < stack.top element. Then store this index .
Code Snippet:
vector<int> findNext(vector<int> values) {
stack<int> st;
vector<int> nextSmall(values.size(), -1);
st.push(0);
for (int i = 1; i < values.size(); i++) {
while (!st.empty() && values[i] < values[st.top()]) {
// change values[i] < values[st.top()] to values[i] > values[st.top()] to find the next greater element.
nextSmall[st.top()] = i;
st.pop();
}
st.push(i);
}
return nextSmall;
}
Solution with O(1) space complexity and O(n) time complexity.
void replace_next_smallest(int a[], int n)
{
int ns = a[n - 1];
for (int i = n - 1; i >= 0; i--) {
if (i == n - 1) {
a[i] = -1;
}
else if (a[i] > ns) {
int t = ns;
ns = a[i];
a[i] = t;
}
else if (a[i] == ns) {
a[i] = a[i + 1];
}
else {
ns = a[i];
a[i] = -1;
}
}
}
Solution With O(n) Time Complexity and O(1) Space Complexity. This Solution is not complex to understand and implemented without stack.
def min_secMin(a,n):
min = a[0]
sec_min = a[1]
for i in range(1,n):
if(a[i]<min):
sec_min = min
min = a[i]
if(a[i]>min and a[i]<sec_min):
sec_min = a[i]
return min,sec_min
Given an array find the next smaller element in array for each element without changing the original order of the elements.
where arr is the array and n is length of the array..
Using Python logic,
def next_smallest_array(arr,n):
for i in range(0,n-1,1):
if arr[i]>arr[i+1]:
arr[i]=arr[i+1]
else:
arr[i]=-1
arr[n-1]=-1
return arr
Find_next_smaller_elements([4,2,1,5,3])
Output is [2, 1, -1, 3, -1]
Find_next_smaller_elements([1,2,3,4,5])
Output is [-1, -1, -1, -1, -1]
Given an Array arr of size 100000, each element 0 <= arr[i] < 100. (not sorted, contains duplicates)
Find out how many triplets (i,j,k) are present such that arr[i] ^ arr[j] ^ arr[k] == 0
Note : ^ is the Xor operator. also 0 <= i <= j <= k <= 100000
I have a feeling i have to calculate the frequencies and do some calculation using the frequency, but i just can't seem to get started.
Any algorithm better than the obvious O(n^3) is welcome. :)
It's not homework. :)
I think the key is you don't need to identify the i,j,k, just count how many.
Initialise an array size 100
Loop though arr, counting how many of each value there are - O(n)
Loop through non-zero elements of the the small array, working out what triples meet the condition - assume the counts of the three numbers involved are A, B, C - the number of combinations in the original arr is (A+B+C)/!A!B!C! - 100**3 operations, but that's still O(1) assuming the 100 is a fixed value.
So, O(n).
Possible O(n^2) solution, if it works: Maintain variable count and two arrays, single[100] and pair[100]. Iterate the arr, and for each element of value n:
update count: count += pair[n]
update pair: iterate array single and for each element of index x and value s != 0 do pair[s^n] += single[x]
update single: single[n]++
In the end count holds the result.
Possible O(100 * n) = O(n) solution.
it solve problem i <= j <= k.
As you know A ^ B = 0 <=> A = B, so
long long calcTripletsCount( const vector<int>& sourceArray )
{
long long res = 0;
vector<int> count(128);
vector<int> countPairs(128);
for(int i = 0; i < sourceArray.size(); i++)
{
count[sourceArray[i]]++; // count[t] contain count of element t in (sourceArray[0]..sourceArray[i])
for(int j = 0; j < count.size(); j++)
countPairs[j ^ sourceArray[i]] += count[j]; // countPairs[t] contain count of pairs p1, p2 (p1 <= p2 for keeping order) where t = sourceArray[i] ^ sourceArray[j]
res += countPairs[sourceArray[i]]; // a ^ b ^ c = 0 if a ^ b = c, we add count of pairs (p1, p2) where sourceArray[p1] ^ sourceArray[p2] = sourceArray[i]. it easy to see that we keep order(p1 <= p2 <= i)
}
return res;
}
Sorry for my bad English...
I have a (simple) O(n^2 log n) solution which takes into account the fact that i, j and k refer to indices, not integers.
A simple first pass allow us to build an array A of 100 values: values -> list of indices, we keep the list sorted for later use. O(n log n)
For each pair i,j such that i <= j, we compute X = arr[i]^arr[j]. We then perform a binary search in A[X] to locate the number of indices k such that k >= j. O(n^2 log n)
I could not find any way to leverage sorting / counting algorithm because they annihilate the index requirement.
Sort the array, keeping a map of new indices to originals. O(nlgn)
Loop over i,j:i<j. O(n^2)
Calculate x = arr[i] ^ arr[j]
Since x ^ arr[k] == 0, arr[k] = x, so binary search k>j for x. O(lgn)
For all found k, print mapped i,j,k
O(n^2 lgn)
Start with a frequency count of the number of occurrences of each number between 1 and 100, as Paul suggests. This produces an array freq[] of length 100.
Next, instead of looping over triples A,B,C from that array and testing the condition A^B^C=0,
loop over pairs A,B with A < B. For each A,B, calculate C=A^B (so that now A^B^C=0), and verify that A < B < C < 100. (Any triple will occur in some order, so this doesn't miss triples. But see below). The running total will look like:
Sum+=freq[A]*freq[B]*freq[C]
The work is O(n) for the frequency count, plus about 5000 for the loop over A < B.
Since every triple of three different numbers A,B,C must occur in some order, this finds each such triple exactly once. Next you'll have to look for triples in which two numbers are equal. But if two numbers are equal and the xor of three of them is 0, the third number must be zero. So this amounts to a secondary linear search for B over the frequency count array, counting occurrences of (A=0, B=C < 100). (Be very careful with this case, and especially careful with the case B=0. The count is not just freq[B] ** 2 or freq[0] ** 3. There is a little combinatorics problem hiding there.)
Hope this helps!
An interesting interview question that a colleague of mine uses:
Suppose that you are given a very long, unsorted list of unsigned 64-bit integers. How would you find the smallest non-negative integer that does not occur in the list?
FOLLOW-UP: Now that the obvious solution by sorting has been proposed, can you do it faster than O(n log n)?
FOLLOW-UP: Your algorithm has to run on a computer with, say, 1GB of memory
CLARIFICATION: The list is in RAM, though it might consume a large amount of it. You are given the size of the list, say N, in advance.
If the datastructure can be mutated in place and supports random access then you can do it in O(N) time and O(1) additional space. Just go through the array sequentially and for every index write the value at the index to the index specified by value, recursively placing any value at that location to its place and throwing away values > N. Then go again through the array looking for the spot where value doesn't match the index - that's the smallest value not in the array. This results in at most 3N comparisons and only uses a few values worth of temporary space.
# Pass 1, move every value to the position of its value
for cursor in range(N):
target = array[cursor]
while target < N and target != array[target]:
new_target = array[target]
array[target] = target
target = new_target
# Pass 2, find first location where the index doesn't match the value
for cursor in range(N):
if array[cursor] != cursor:
return cursor
return N
Here's a simple O(N) solution that uses O(N) space. I'm assuming that we are restricting the input list to non-negative numbers and that we want to find the first non-negative number that is not in the list.
Find the length of the list; lets say it is N.
Allocate an array of N booleans, initialized to all false.
For each number X in the list, if X is less than N, set the X'th element of the array to true.
Scan the array starting from index 0, looking for the first element that is false. If you find the first false at index I, then I is the answer. Otherwise (i.e. when all elements are true) the answer is N.
In practice, the "array of N booleans" would probably be encoded as a "bitmap" or "bitset" represented as a byte or int array. This typically uses less space (depending on the programming language) and allows the scan for the first false to be done more quickly.
This is how / why the algorithm works.
Suppose that the N numbers in the list are not distinct, or that one or more of them is greater than N. This means that there must be at least one number in the range 0 .. N - 1 that is not in the list. So the problem of find the smallest missing number must therefore reduce to the problem of finding the smallest missing number less than N. This means that we don't need to keep track of numbers that are greater or equal to N ... because they won't be the answer.
The alternative to the previous paragraph is that the list is a permutation of the numbers from 0 .. N - 1. In this case, step 3 sets all elements of the array to true, and step 4 tells us that the first "missing" number is N.
The computational complexity of the algorithm is O(N) with a relatively small constant of proportionality. It makes two linear passes through the list, or just one pass if the list length is known to start with. There is no need to represent the hold the entire list in memory, so the algorithm's asymptotic memory usage is just what is needed to represent the array of booleans; i.e. O(N) bits.
(By contrast, algorithms that rely on in-memory sorting or partitioning assume that you can represent the entire list in memory. In the form the question was asked, this would require O(N) 64-bit words.)
#Jorn comments that steps 1 through 3 are a variation on counting sort. In a sense he is right, but the differences are significant:
A counting sort requires an array of (at least) Xmax - Xmin counters where Xmax is the largest number in the list and Xmin is the smallest number in the list. Each counter has to be able to represent N states; i.e. assuming a binary representation it has to have an integer type (at least) ceiling(log2(N)) bits.
To determine the array size, a counting sort needs to make an initial pass through the list to determine Xmax and Xmin.
The minimum worst-case space requirement is therefore ceiling(log2(N)) * (Xmax - Xmin) bits.
By contrast, the algorithm presented above simply requires N bits in the worst and best cases.
However, this analysis leads to the intuition that if the algorithm made an initial pass through the list looking for a zero (and counting the list elements if required), it would give a quicker answer using no space at all if it found the zero. It is definitely worth doing this if there is a high probability of finding at least one zero in the list. And this extra pass doesn't change the overall complexity.
EDIT: I've changed the description of the algorithm to use "array of booleans" since people apparently found my original description using bits and bitmaps to be confusing.
Since the OP has now specified that the original list is held in RAM and that the computer has only, say, 1GB of memory, I'm going to go out on a limb and predict that the answer is zero.
1GB of RAM means the list can have at most 134,217,728 numbers in it. But there are 264 = 18,446,744,073,709,551,616 possible numbers. So the probability that zero is in the list is 1 in 137,438,953,472.
In contrast, my odds of being struck by lightning this year are 1 in 700,000. And my odds of getting hit by a meteorite are about 1 in 10 trillion. So I'm about ten times more likely to be written up in a scientific journal due to my untimely death by a celestial object than the answer not being zero.
As pointed out in other answers you can do a sort, and then simply scan up until you find a gap.
You can improve the algorithmic complexity to O(N) and keep O(N) space by using a modified QuickSort where you eliminate partitions which are not potential candidates for containing the gap.
On the first partition phase, remove duplicates.
Once the partitioning is complete look at the number of items in the lower partition
Is this value equal to the value used for creating the partition?
If so then it implies that the gap is in the higher partition.
Continue with the quicksort, ignoring the lower partition
Otherwise the gap is in the lower partition
Continue with the quicksort, ignoring the higher partition
This saves a large number of computations.
To illustrate one of the pitfalls of O(N) thinking, here is an O(N) algorithm that uses O(1) space.
for i in [0..2^64):
if i not in list: return i
print "no 64-bit integers are missing"
Since the numbers are all 64 bits long, we can use radix sort on them, which is O(n). Sort 'em, then scan 'em until you find what you're looking for.
if the smallest number is zero, scan forward until you find a gap. If the smallest number is not zero, the answer is zero.
For a space efficient method and all values are distinct you can do it in space O( k ) and time O( k*log(N)*N ). It's space efficient and there's no data moving and all operations are elementary (adding subtracting).
set U = N; L=0
First partition the number space in k regions. Like this:
0->(1/k)*(U-L) + L, 0->(2/k)*(U-L) + L, 0->(3/k)*(U-L) + L ... 0->(U-L) + L
Find how many numbers (count{i}) are in each region. (N*k steps)
Find the first region (h) that isn't full. That means count{h} < upper_limit{h}. (k steps)
if h - count{h-1} = 1 you've got your answer
set U = count{h}; L = count{h-1}
goto 2
this can be improved using hashing (thanks for Nic this idea).
same
First partition the number space in k regions. Like this:
L + (i/k)->L + (i+1/k)*(U-L)
inc count{j} using j = (number - L)/k (if L < number < U)
find first region (h) that doesn't have k elements in it
if count{h} = 1 h is your answer
set U = maximum value in region h L = minimum value in region h
This will run in O(log(N)*N).
I'd just sort them then run through the sequence until I find a gap (including the gap at the start between zero and the first number).
In terms of an algorithm, something like this would do it:
def smallest_not_in_list(list):
sort(list)
if list[0] != 0:
return 0
for i = 1 to list.last:
if list[i] != list[i-1] + 1:
return list[i-1] + 1
if list[list.last] == 2^64 - 1:
assert ("No gaps")
return list[list.last] + 1
Of course, if you have a lot more memory than CPU grunt, you could create a bitmask of all possible 64-bit values and just set the bits for every number in the list. Then look for the first 0-bit in that bitmask. That turns it into an O(n) operation in terms of time but pretty damned expensive in terms of memory requirements :-)
I doubt you could improve on O(n) since I can't see a way of doing it that doesn't involve looking at each number at least once.
The algorithm for that one would be along the lines of:
def smallest_not_in_list(list):
bitmask = mask_make(2^64) // might take a while :-)
mask_clear_all (bitmask)
for i = 1 to list.last:
mask_set (bitmask, list[i])
for i = 0 to 2^64 - 1:
if mask_is_clear (bitmask, i):
return i
assert ("No gaps")
Sort the list, look at the first and second elements, and start going up until there is a gap.
We could use a hash table to hold the numbers. Once all numbers are done, run a counter from 0 till we find the lowest. A reasonably good hash will hash and store in constant time, and retrieves in constant time.
for every i in X // One scan Θ(1)
hashtable.put(i, i); // O(1)
low = 0;
while (hashtable.get(i) <> null) // at most n+1 times
low++;
print low;
The worst case if there are n elements in the array, and are {0, 1, ... n-1}, in which case, the answer will be obtained at n, still keeping it O(n).
You can do it in O(n) time and O(1) additional space, although the hidden factor is quite large. This isn't a practical way to solve the problem, but it might be interesting nonetheless.
For every unsigned 64-bit integer (in ascending order) iterate over the list until you find the target integer or you reach the end of the list. If you reach the end of the list, the target integer is the smallest integer not in the list. If you reach the end of the 64-bit integers, every 64-bit integer is in the list.
Here it is as a Python function:
def smallest_missing_uint64(source_list):
the_answer = None
target = 0L
while target < 2L**64:
target_found = False
for item in source_list:
if item == target:
target_found = True
if not target_found and the_answer is None:
the_answer = target
target += 1L
return the_answer
This function is deliberately inefficient to keep it O(n). Note especially that the function keeps checking target integers even after the answer has been found. If the function returned as soon as the answer was found, the number of times the outer loop ran would be bound by the size of the answer, which is bound by n. That change would make the run time O(n^2), even though it would be a lot faster.
Thanks to egon, swilden, and Stephen C for my inspiration. First, we know the bounds of the goal value because it cannot be greater than the size of the list. Also, a 1GB list could contain at most 134217728 (128 * 2^20) 64-bit integers.
Hashing part
I propose using hashing to dramatically reduce our search space. First, square root the size of the list. For a 1GB list, that's N=11,586. Set up an integer array of size N. Iterate through the list, and take the square root* of each number you find as your hash. In your hash table, increment the counter for that hash. Next, iterate through your hash table. The first bucket you find that is not equal to it's max size defines your new search space.
Bitmap part
Now set up a regular bit map equal to the size of your new search space, and again iterate through the source list, filling out the bitmap as you find each number in your search space. When you're done, the first unset bit in your bitmap will give you your answer.
This will be completed in O(n) time and O(sqrt(n)) space.
(*You could use use something like bit shifting to do this a lot more efficiently, and just vary the number and size of buckets accordingly.)
Well if there is only one missing number in a list of numbers, the easiest way to find the missing number is to sum the series and subtract each value in the list. The final value is the missing number.
int i = 0;
while ( i < Array.Length)
{
if (Array[i] == i + 1)
{
i++;
}
if (i < Array.Length)
{
if (Array[i] <= Array.Length)
{//SWap
int temp = Array[i];
int AnoTemp = Array[temp - 1];
Array[temp - 1] = temp;
Array[i] = AnoTemp;
}
else
i++;
}
}
for (int j = 0; j < Array.Length; j++)
{
if (Array[j] > Array.Length)
{
Console.WriteLine(j + 1);
j = Array.Length;
}
else
if (j == Array.Length - 1)
Console.WriteLine("Not Found !!");
}
}
Here's my answer written in Java:
Basic Idea:
1- Loop through the array throwing away duplicate positive, zeros, and negative numbers while summing up the rest, getting the maximum positive number as well, and keep the unique positive numbers in a Map.
2- Compute the sum as max * (max+1)/2.
3- Find the difference between the sums calculated at steps 1 & 2
4- Loop again from 1 to the minimum of [sums difference, max] and return the first number that is not in the map populated in step 1.
public static int solution(int[] A) {
if (A == null || A.length == 0) {
throw new IllegalArgumentException();
}
int sum = 0;
Map<Integer, Boolean> uniqueNumbers = new HashMap<Integer, Boolean>();
int max = A[0];
for (int i = 0; i < A.length; i++) {
if(A[i] < 0) {
continue;
}
if(uniqueNumbers.get(A[i]) != null) {
continue;
}
if (A[i] > max) {
max = A[i];
}
uniqueNumbers.put(A[i], true);
sum += A[i];
}
int completeSum = (max * (max + 1)) / 2;
for(int j = 1; j <= Math.min((completeSum - sum), max); j++) {
if(uniqueNumbers.get(j) == null) { //O(1)
return j;
}
}
//All negative case
if(uniqueNumbers.isEmpty()) {
return 1;
}
return 0;
}
As Stephen C smartly pointed out, the answer must be a number smaller than the length of the array. I would then find the answer by binary search. This optimizes the worst case (so the interviewer can't catch you in a 'what if' pathological scenario). In an interview, do point out you are doing this to optimize for the worst case.
The way to use binary search is to subtract the number you are looking for from each element of the array, and check for negative results.
I like the "guess zero" apprach. If the numbers were random, zero is highly probable. If the "examiner" set a non-random list, then add one and guess again:
LowNum=0
i=0
do forever {
if i == N then leave /* Processed entire array */
if array[i] == LowNum {
LowNum++
i=0
}
else {
i++
}
}
display LowNum
The worst case is n*N with n=N, but in practice n is highly likely to be a small number (eg. 1)
I am not sure if I got the question. But if for list 1,2,3,5,6 and the missing number is 4, then the missing number can be found in O(n) by:
(n+2)(n+1)/2-(n+1)n/2
EDIT: sorry, I guess I was thinking too fast last night. Anyway, The second part should actually be replaced by sum(list), which is where O(n) comes. The formula reveals the idea behind it: for n sequential integers, the sum should be (n+1)*n/2. If there is a missing number, the sum would be equal to the sum of (n+1) sequential integers minus the missing number.
Thanks for pointing out the fact that I was putting some middle pieces in my mind.
Well done Ants Aasma! I thought about the answer for about 15 minutes and independently came up with an answer in a similar vein of thinking to yours:
#define SWAP(x,y) { numerictype_t tmp = x; x = y; y = tmp; }
int minNonNegativeNotInArr (numerictype_t * a, size_t n) {
int m = n;
for (int i = 0; i < m;) {
if (a[i] >= m || a[i] < i || a[i] == a[a[i]]) {
m--;
SWAP (a[i], a[m]);
continue;
}
if (a[i] > i) {
SWAP (a[i], a[a[i]]);
continue;
}
i++;
}
return m;
}
m represents "the current maximum possible output given what I know about the first i inputs and assuming nothing else about the values until the entry at m-1".
This value of m will be returned only if (a[i], ..., a[m-1]) is a permutation of the values (i, ..., m-1). Thus if a[i] >= m or if a[i] < i or if a[i] == a[a[i]] we know that m is the wrong output and must be at least one element lower. So decrementing m and swapping a[i] with the a[m] we can recurse.
If this is not true but a[i] > i then knowing that a[i] != a[a[i]] we know that swapping a[i] with a[a[i]] will increase the number of elements in their own place.
Otherwise a[i] must be equal to i in which case we can increment i knowing that all the values of up to and including this index are equal to their index.
The proof that this cannot enter an infinite loop is left as an exercise to the reader. :)
The Dafny fragment from Ants' answer shows why the in-place algorithm may fail. The requires pre-condition describes that the values of each item must not go beyond the bounds of the array.
method AntsAasma(A: array<int>) returns (M: int)
requires A != null && forall N :: 0 <= N < A.Length ==> 0 <= A[N] < A.Length;
modifies A;
{
// Pass 1, move every value to the position of its value
var N := A.Length;
var cursor := 0;
while (cursor < N)
{
var target := A[cursor];
while (0 <= target < N && target != A[target])
{
var new_target := A[target];
A[target] := target;
target := new_target;
}
cursor := cursor + 1;
}
// Pass 2, find first location where the index doesn't match the value
cursor := 0;
while (cursor < N)
{
if (A[cursor] != cursor)
{
return cursor;
}
cursor := cursor + 1;
}
return N;
}
Paste the code into the validator with and without the forall ... clause to see the verification error. The second error is a result of the verifier not being able to establish a termination condition for the Pass 1 loop. Proving this is left to someone who understands the tool better.
Here's an answer in Java that does not modify the input and uses O(N) time and N bits plus a small constant overhead of memory (where N is the size of the list):
int smallestMissingValue(List<Integer> values) {
BitSet bitset = new BitSet(values.size() + 1);
for (int i : values) {
if (i >= 0 && i <= values.size()) {
bitset.set(i);
}
}
return bitset.nextClearBit(0);
}
def solution(A):
index = 0
target = []
A = [x for x in A if x >=0]
if len(A) ==0:
return 1
maxi = max(A)
if maxi <= len(A):
maxi = len(A)
target = ['X' for x in range(maxi+1)]
for number in A:
target[number]= number
count = 1
while count < maxi+1:
if target[count] == 'X':
return count
count +=1
return target[count-1] + 1
Got 100% for the above solution.
1)Filter negative and Zero
2)Sort/distinct
3)Visit array
Complexity: O(N) or O(N * log(N))
using Java8
public int solution(int[] A) {
int result = 1;
boolean found = false;
A = Arrays.stream(A).filter(x -> x > 0).sorted().distinct().toArray();
//System.out.println(Arrays.toString(A));
for (int i = 0; i < A.length; i++) {
result = i + 1;
if (result != A[i]) {
found = true;
break;
}
}
if (!found && result == A.length) {
//result is larger than max element in array
result++;
}
return result;
}
An unordered_set can be used to store all the positive numbers, and then we can iterate from 1 to length of unordered_set, and see the first number that does not occur.
int firstMissingPositive(vector<int>& nums) {
unordered_set<int> fre;
// storing each positive number in a hash.
for(int i = 0; i < nums.size(); i +=1)
{
if(nums[i] > 0)
fre.insert(nums[i]);
}
int i = 1;
// Iterating from 1 to size of the set and checking
// for the occurrence of 'i'
for(auto it = fre.begin(); it != fre.end(); ++it)
{
if(fre.find(i) == fre.end())
return i;
i +=1;
}
return i;
}
Solution through basic javascript
var a = [1, 3, 6, 4, 1, 2];
function findSmallest(a) {
var m = 0;
for(i=1;i<=a.length;i++) {
j=0;m=1;
while(j < a.length) {
if(i === a[j]) {
m++;
}
j++;
}
if(m === 1) {
return i;
}
}
}
console.log(findSmallest(a))
Hope this helps for someone.
With python it is not the most efficient, but correct
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import datetime
# write your code in Python 3.6
def solution(A):
MIN = 0
MAX = 1000000
possible_results = range(MIN, MAX)
for i in possible_results:
next_value = (i + 1)
if next_value not in A:
return next_value
return 1
test_case_0 = [2, 2, 2]
test_case_1 = [1, 3, 44, 55, 6, 0, 3, 8]
test_case_2 = [-1, -22]
test_case_3 = [x for x in range(-10000, 10000)]
test_case_4 = [x for x in range(0, 100)] + [x for x in range(102, 200)]
test_case_5 = [4, 5, 6]
print("---")
a = datetime.datetime.now()
print(solution(test_case_0))
print(solution(test_case_1))
print(solution(test_case_2))
print(solution(test_case_3))
print(solution(test_case_4))
print(solution(test_case_5))
def solution(A):
A.sort()
j = 1
for i, elem in enumerate(A):
if j < elem:
break
elif j == elem:
j += 1
continue
else:
continue
return j
this can help:
0- A is [5, 3, 2, 7];
1- Define B With Length = A.Length; (O(1))
2- initialize B Cells With 1; (O(n))
3- For Each Item In A:
if (B.Length <= item) then B[Item] = -1 (O(n))
4- The answer is smallest index in B such that B[index] != -1 (O(n))