Interview - finding even sum pair in an array - c++

Given an array , how would you return the number of pairs which sum to an even number?
For example:
a[] = { 2 , -6 , 1, 3, 5 }
In this array , the nos which pair to an even sum are
(2,-6), (1,3) , (1,5), (3,5)
Function should return 4 as there are 4 pairs or -1 if none.
Expected time complexity - O(N) worst case
Expected space complexity - O(N) worst case
Approach 1:
Brute force
Start with the first number
Start with second number
assign the sum to a temp variable
check if the temp is even
If it is increment evenPair count
else
increment the second index
Here the time complexity is O(N2)

int odd = 0, even = 0;
for (int i = 0; i < n; i++) {
if (a[i] % 2 == 0) {
even++;
} else {
odd++;
}
}
int answer = (odd * (odd - 1) + even * (even - 1)) / 2;

If a and b are even, a + b is even.
If they're odd, a + b is also even.
If one is odd and one is even, a + b is odd.
This means that we don't need to perform any additions, we only need to know how many numbers there are of each kind.
Finding this out takes linear time.
If you have k numbers, there are k - 1 pairs that include the first number, k - 2 involving the second, and so on.
Using the familiar summation formula, sum(1 .. n) = (n * (n + 1)) / 2,
let
ne = number of even numbers
no = number of odd numbers
then,
number of pairs = (ne-1) * ne / 2 + (no-1) * no / 2
= ((ne-1)*ne + (no-1)*no) / 2
That computation runs in constant time, so the time complexity is still linear.
And we only need a constant amount of extra space, which is better than the requirements.
Possible followup interview questions possibly worth thinking about:
What happens to the complexities (both in time and space) if:
duplicates are only counted once, i.e. {1,1,1,2} only has two such pairs?
we disregard order, i.e. (1,2) and (2,1) are the "same" pair?

If to use standard algorithms then the code can look the following way
#include <iostream>
#include <utility>
#include <numeric>
#include <iterator>
int main()
{
int a[] = { 2 , -6 , 1, 3, 5 };
typedef size_t Odd, Even;
auto p = std::accumulate( std::begin( a ), std::end( a ),
std::pair<Odd, Even>( 0, 0 ),
[]( std::pair<Odd, Even> &acc, int x )
{
return x & 1 ? ++acc.first : ++acc.second, acc;
} );
std::cout << "There are "
<< ( p.first * ( p.first - 1 ) + p.second * ( p.second - 1 ) ) / 2
<< " even sums" << std::endl;
return 0;
}
The output is
There are 4 even sums
Take into account that n! / ( 2! * ( n - 2 )! ) is equivalent to ( n - 1 ) * n / 2
The advantage of using the standard algorithm is that you can use any subrange of a sequence. Also you can use a standard stream input because std::accumulate uses input iterator.
Also it would be much better if in the description of the assignment there would be written that the function should return 0 instead of -1 if there is no even sums among elements of an array.
It is not shameful to show this code in an interview though I advice never do any assignments in an interview. Interview is not an exam.

A different approach would be compute the number of sums by even and odds, and then sum then together
int sumO = 0 , sumE = 0 , numO = 0 , numE = 0;
for (int i=0; i < 5; i++)
{
if (a[i] % 2 == 0)
{
sumE += numE;
numE ++;
}
else
{
sumO += numO;
numO ++;
}
}
printf ("Total: %d\n", sumO + sumE);

I got this question from an interview.
No need to total even and odd both separate.
public static int evenTotalPairs(int[] A) {
int size = A.length;
int evens = 0;
for(int i = 0; i < size; i++ ){
if(A[i] % 2 == 0){
evens++;
}
}
return evens*(evens-1)/2 + (size-evens)*(size-evens-1)/2;
}

Related

Need optimization tips for a subset sum like problem with a big constraint

Given a number 1 <= N <= 3*10^5, count all subsets in the set {1, 2, ..., N-1} that sum up to N. This is essentially a modified version of the subset sum problem, but with a modification that the sum and number of elements are the same, and that the set/array increases linearly by 1 to N-1.
I think i have solved this using dp ordered map and inclusion/exclusion recursive algorithm, but due to the time and space complexity i can't compute more than 10000 elements.
#include <iostream>
#include <chrono>
#include <map>
#include "bigint.h"
using namespace std;
//2d hashmap to store values from recursion; keys- i & sum; value- count
map<pair<int, int>, bigint> hmap;
bigint counter(int n, int i, int sum){
//end case
if(i == 0){
if(sum == 0){
return 1;
}
return 0;
}
//alternative end case if its sum is zero before it has finished iterating through all of the possible combinations
if(sum == 0){
return 1;
}
//case if the result of the recursion is already in the hashmap
if(hmap.find(make_pair(i, sum)) != hmap.end()){
return hmap[make_pair(i, sum)];
}
//only proceed further recursion if resulting sum wouldnt be negative
if(sum - i < 0){
//optimization that skips unecessary recursive branches
return hmap[make_pair(i, sum)] = counter(n, sum, sum);
}
else{
//include the number dont include the number
return hmap[make_pair(i, sum)] = counter(n, i - 1, sum - i) + counter(n, i - 1, sum);
}
}
The function has starting values of N, N-1, and N, indicating number of elements, iterator(which decrements) and the sum of the recursive branch(which decreases with every included value).
This is the code that calculates the number of the subsets. for input of 3000 it takes around ~22 seconds to output the result which is 40 digits long. Because of the long digits i had to use an arbitrary precision library bigint from rgroshanrg, which works fine for values less than ~10000. Testing beyond that gives me a segfault on line 28-29, maybe due to the stored arbitrary precision values becoming too big and conflicting in the map. I need to somehow up this code so it can work with values beyond 10000 but i am stumped with it. Any ideas or should i switch towards another algorithm and data storage?
Here is a different algorithm, described in a paper by Evangelos Georgiadis, "Computing Partition Numbers q(n)":
std::vector<BigInt> RestrictedPartitionNumbers(int n)
{
std::vector<BigInt> q(n, 0);
// initialize q with A010815
for (int i = 0; ; i++)
{
int n0 = i * (3 * i - 1) >> 1;
if (n0 >= q.size())
break;
q[n0] = 1 - 2 * (i & 1);
int n1 = i * (3 * i + 1) >> 1;
if (n1 < q.size())
q[n1] = 1 - 2 * (i & 1);
}
// construct A000009 as per "Evangelos Georgiadis, Computing Partition Numbers q(n)"
for (size_t k = 0; k < q.size(); k++)
{
size_t j = 1;
size_t m = k + 1;
while (m < q.size())
{
if ((j & 1) != 0)
q[m] += q[k] << 1;
else
q[m] -= q[k] << 1;
j++;
m = k + j * j;
}
}
return q;
}
It's not the fastest algorithm out there, and this took about half a minute for on my computer for n = 300000. But you only need to do it once (since it computes all partition numbers up to some bound) and it doesn't take a lot of memory (a bit over 150MB).
The results go up to but excluding n, and they assume that for each number, that number itself is allowed to be a partition of itself eg the set {4} is a partition of the number 4, in your definition of the problem you excluded that case so you need to subtract 1 from the result.
Maybe there's a nicer way to express A010815, that part of the code isn't slow though, I just think it looks bad.

Speed problem for summation (sum of divisors)

I should implement this summation in C ++. I have tried with this code, but with very high numbers up to 10 ^ 12 it takes too long.
The summation is:
For any positive integer k, let d(k) denote the number of positive divisors of k (including 1 and k itself).
For example, for the number 4: 1 has 1 divisor, 2 has two divisors, 3 has two divisors, and 4 has three divisors. So the result would be 8.
This is my code:
#include <iostream>
#include <algorithm>
using namespace std;
int findDivisors(long long n)
{
int c=0;
for(int j=1;j*j<=n;j++)
{
if(n%j==0)
{
c++;
if(j!=(n/j))
{
c++;
}
}
}
return c;
}
long long compute(long long n)
{
long long sum=0;
for(int i=1; i<=n; i++)
{
sum += (findDivisors(i));
}
return sum;
}
int main()
{
int n, divisors;
freopen("input.txt", "r", stdin);
freopen("output.txt", "w", stdout);
cin >> n;
cout << compute(n);
}
I think it's not just a simple optimization problem, but maybe I should change the algorithm entirely.
Would anyone have any ideas to speed it up? Thank you.
largest_prime_is_463035818's answer shows an O(N) solution, but the OP is trying to solve this problem
with very high numbers up to 1012.
The following is an O(N1/2) algorithm, based on some observations about the sum
n/1 + n/2 + n/3 + ... + n/n
In particular, we can count the number of terms with a specific value.
Consider all the terms n/k where k > n/2. There are n/2 of those and all are equal to 1 (integer division), so that their sum is n/2.
Similar considerations hold for the other dividends, so that we can write the following function
long long count_divisors(long long n)
{
auto sum{ n };
for (auto i{ 1ll }, k_old{ n }, k{ n }; i < k ; ++i, k_old = k)
{ // ^^^^^ it goes up to sqrt(n)
k = n / (i + 1);
sum += (k_old - k) * i;
if (i == k)
break;
sum += k;
}
return sum;
}
Here it is tested against the O(N) algorithm, the only difference in the results beeing the corner cases n = 0 and n = 1.
Edit
Thanks again to largest_prime_is_463035818, who linked the Wikipedia page about the divisor summatory function, where both an O(N) and an O(sqrt(N)) algorithm are mentioned.
An implementation of the latter may look like this
auto divisor_summatory(long long n)
{
auto sum{ 0ll };
auto k{ 1ll };
for ( ; k <= n / k; ++k )
{
sum += n / k;
}
--k;
return 2 * sum - k * k;
}
They also add this statement:
Finding a closed form for this summed expression seems to be beyond the techniques available, but it is possible to give approximations. The leading behavior of the series is given by
D(x) = xlogx + x(2γ - 1) + Δ(x)
where γ is the Euler–Mascheroni constant, and the error term is Δ(x) = O(sqrt(x)).
I used your brute force approach as reference to have test cases. The ones I used are
compute(12) == 35
cpmpute(100) == 482
Don't get confused by computing factorizations. There are some tricks one can play when factorizing numbers, but you actually don't need any of that. The solution is a plain simple O(N) loop:
#include <iostream>
#include <limits>
long long compute(long long n){
long long sum = n+1;
for (long long i=2; i < n ; ++i){
sum += n/i;
}
return sum;
}
int main()
{
std::cout << compute(12) << "\n";
std::cout << compute(100) << "\n";
}
Output:
35
482
Why does this work?
The key is in Marc Glisse's comment:
As often with this kind of problem, this sum actually counts pairs x,
y where x divides y, and the sum is arranged to count first all x
corresponding to a fixed y, but nothing says you have to keep it that
way.
I could stop here, because the comment already explains it all. Though, if it didn't click yet...
The trick is to realize that it is much simpler to count divisors of all numbers up to n rather than n-times counting divisors of individual numbers and take the sum.
You don't need to care about factorizations of eg 123123123 or 52323423 to count all divisors up to 10000000000. All you need is a change of perspective. Instead of trying to factorize numbers, consider the divisors. How often does the divisor 1 appear up to n? Simple: n-times. How often does the divisor 2 appear? Still simple: n/2 times, because every second number is divisible by 2. Divisor 3? Every 3rd number is divisible by 3. I hope you can see the pattern already.
You could even reduce the loop to only loop till n/2, because bigger numbers obviously appear only once as divisor. Though I didn't bother to go further, because the biggest change is from your O(N * sqrt(N)) to O(N).
Let's start off with some math and reduce the O(n * sq(n)) factorization to O(n * log(log(n))) and for counting the sum of divisors the overall complexity is O(n * log(log(n)) + n * n^(1/3)).
For instance:
In Codeforces himanshujaju explains how we can optimize the solution of finding divisors of a number.
I am simplifying it a little bit.
Let, n as the product of three numbers p, q, and r.
so assume p * q * r = n, where p <= q <= r.
The maximum value of p = n^(1/3).
Now we can loop over all prime numbers in a range [2, n^(1/3)]
and try to reduce the time complexity of prime factorization.
We will split our number n into two numbers x and y => x * y = n.
And x contains prime factors up to n^(1/3) and y deals with higher prime factors greater than n^(1/3).
Thus gcd(x, y) = 1.
Now define F(n) as the number of prime factors of n.
From multiplicative rules, we can say that
F(x * y) = F(x) * F(y), if gcd(x, y) = 1.
For finding F(n) => F(x * y) = F(x) * F(y)
So first find F(x) then F(y) will F(n/x)
And there will 3 cases to cover for y:
1. y is a prime number: F(y) = 2.
2. y is the square of a prime number: F(y) = 3.
3. y is a product of two distinct prime numbers: F(y) = 4.
So once we are done with finding F(x) and F(y), we are also done with finding F(x * y) or F(n).
In Cp-Algorithm there is also a nice explanation of how to count the number of divisors on a number. And also in GeeksForGeeks a nice coding example of how to count the number of divisors of a number in an efficient way. One can check the articles and can generate a nice solution to this problem.
C++ implementation
#include <bits/stdc++.h>
using namespace std;
const int maxn = 1e6 + 11;
bool prime[maxn];
bool primesquare[maxn];
int table[maxn]; // for storing primes
void SieveOfEratosthenes()
{
for(int i = 2; i < maxn; i++){
prime[i] = true;
}
for(int i = 0; i < maxn; i++){
primesquare[i] = false;
}
// 1 is not a prime number
prime[1] = false;
for(int p = 2; p * p < maxn; p++){
// If prime[p] is not changed, then
// it is a prime
if(prime[p] == true){
// Update all multiples of p
for(int i = p * 2; i < maxn; i += p){
prime[i] = false;
}
}
}
int j = 0;
for(int p = 2; p < maxn; p++) {
if (prime[p]) {
// Storing primes in an array
table[j] = p;
// Update value in primesquare[p * p],
// if p is prime.
if(p < maxn / p) primesquare[p * p] = true;
j++;
}
}
}
// Function to count divisors
int countDivisors(int n)
{
// If number is 1, then it will have only 1
// as a factor. So, total factors will be 1.
if (n == 1)
return 1;
// ans will contain total number of distinct
// divisors
int ans = 1;
// Loop for counting factors of n
for(int i = 0;; i++){
// table[i] is not less than cube root n
if(table[i] * table[i] * table[i] > n)
break;
// Calculating power of table[i] in n.
int cnt = 1; // cnt is power of prime table[i] in n.
while (n % table[i] == 0){ // if table[i] is a factor of n
n = n / table[i];
cnt = cnt + 1; // incrementing power
}
// Calculating the number of divisors
// If n = a^p * b^q then total divisors of n
// are (p+1)*(q+1)
ans = ans * cnt;
}
// if table[i] is greater than cube root of n
// First case
if (prime[n])
ans = ans * 2;
// Second case
else if (primesquare[n])
ans = ans * 3;
// Third case
else if (n != 1)
ans = ans * 4;
return ans; // Total divisors
}
int main()
{
SieveOfEratosthenes();
int sum = 0;
int n = 5;
for(int i = 1; i <= n; i++){
sum += countDivisors(i);
}
cout << sum << endl;
return 0;
}
Output
n = 4 => 8
n = 5 => 10
Complexity
Time complexity: O(n * log(log(n)) + n * n^(1/3))
Space complexity: O(n)
Thanks, #largest_prime_is_463035818 for pointing out my mistake.

Maximum value of M digits out of N digits [duplicate]

This question already has answers here:
How to get the least number after deleting k digits from the input number
(11 answers)
Closed 6 years ago.
I am trying to code a program that can do something like this:
in:
5 4
1 9 9 9 0
out:
9990
and i have a problem. It doesnt work on any set of numbers. For example it works for the one above, but it doesnt work for this one:
in:
15 9
2 9 3 6 5 8 8 8 8 7 2 2 8 1 4
out: 988887814
2 9 3 6 5 8 8 8 8 7 2 2 8 1 4
I did this with a vector approach and it works for any set of numbers, but i'm trying to do it a stack for a better complexity.
EDIT ---- MODIFIED FOR STD::STACK
Code for method using stack:
#include <iostream>
#include <fstream>
#include <stack>
using namespace std;
ifstream in("trompeta.in");
ofstream out("trompeta.out");
void reverseStack(stack<char> st) {
if(!st.empty())
{
char x = st.top();
st.pop();
reverseStack(st);
out<<x;
}
return;
}
int main()
{
int n,m,count=1;
stack <char> st;
char x;
in>>n>>m;
in>>x;
st.push(x);
for(int i=1; i<n; i++)
{
in>>x;
if(st.top()<x && count+n-i-1>=m)
{
st.pop();
st.push(x);
}
else
{
st.push(x);
count++;
if (count>m-1) break;
}
};
reverseStack(st);
}
Code for method using vectors:
#include <iostream>
#include <fstream>
using namespace std;
ifstream in ( "trompeta.in" );
ofstream out ( "trompeta.out" );
int main ()
{
int i = 0, N, M, max, j, p = 0, var;
in >> N >> M;
char* v = new char[N];
char* a = new char[M];
in >> v;
var = M;
max = v[0];
for ( i = 0; i < M; i++ )
{
for ( j = p ; j < N-var+1; j++ )
{
if ( v[j] > max )
{
max = v[j];
p = j;
}
}
var--;
a[i] = max;
max = v[p+1];
p = p+1;
}
for ( i = 0; i < M; i++ )
out << a[i]-'0';
}
Can any1 help me to get the STACK code working?
Using the fact that the most significant digit completely trumps all other digets except in place of a tie, I would look at the first (N-M+1) digits, find the largest single digit in that range.
If it occurs once, the first digit is locked in. Discard the digits which occur prior to that position, and you repeat for "maximum value of M-1 numbers of out N-position" to find the remaining digits of the answer. (or N-position-1, if position is zero based)
If it occurs multiple times, then recursively find "maximum value of M-1 numbers out of N-position" for each, then select the largest single result from these. There can be at most N such matches.
I forgot to mention, if N==M, you are also done.
proof of recursion:
Computing the value of the sub-match will always select M-1 digits. When M is 1, you only need to select the largest of a few positions, and have no more recursion. This is true for both cases. Also the "select from" steps always contain no more than N choices, because they are always based on selecting one most significant digit.
------------------ how you might do it with a stack ----------------
An actual implementation using a stack would be based on an object which contains the entire state of the problem, at each step, like so:
struct data { // require: n == digits.size()
int n, m;
std::string digits;
bool operator<(const data &rhs){ return digits < rhs.digits; }
};
The point of this is not just to store the original problem, but to have a way to represent any subproblem, which you can push and pop on a stack. The stack itself is not really important, here, because it is used to pick the one best result within a specific layer. Recursion handles most of the work.
Here is the top level function which hides the data struct:
std::string select_ordered_max(int n, int m, std::string digits) {
if (n < m || (int)digits.size() != n)
return "size wrong";
data d{ n, m, digits };
data answer = select_ordered_max(d);
return answer.digits;
}
and a rough pseudocode of the recursive workhorse
data select_ordered_max(data original){
// check trivial return conditions
// determine char most_significant
// push all subproblems that satisfy most_significant
//(special case where m==1)
// pop subproblems, remembering best
return answer {original.m, original.m, std::string(1, most_significant) + best_submatch.digits };
}
String comparison works on numbers when you only compare strings of the exact same length, which is the case here.
Yes, I know having n and m is redundant with digits.size(), but I didn't want to work too hard. Including it twice simplified some recursion checks. The actual implementation only pushed a candidate to the stack if it passed the max digit check for that level of recursion. This allowed me to get the correct 9 digit answer from 15 digits of input with only 28 candidates pushed to the stack (and them popped during max-select).
Now your code has quite a few issues, but rather than focusing on those lets answer the question. Let's say that your code has been corrected to give us:
const size_t M where M is the number of digits expected in our output
const vector<int> v which is the input set of numbers of size N
You just always want to pick the highest value most significant number remaining. So we'll keep an end iterator to prevent us from picking a digit that wouldn't leave us with enough digits to finish the number, and use max_element to select:
const int pow10[] = { 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000 };
auto maximum = 0;
auto end = prev(cend(v), M - 1);
auto it = max_element(cbegin(v), end);
for (auto i = M - 1; i > 0; --i) {
maximum += *it * pow10[i];
advance(end, 1);
it = max_element(next(it), end);
}
maximum += *it;
Live Example
This code depends upon M being greater than 0 and less than N and less than log10(numeric_limits<int>::max())
EDIT: Sad to say this solves the consecutive digits problem, after edits the question wants subsequent digits, but not necessarily consecutive
So the little known numeric library provides inner_product which seems like just the tool for this job. Now your code has quite a few issues, but rather than focusing on those lets answer the question. Let's say that your code has been corrected to give us:
vector<int> foo(M) where M is the number of digits expected in our output
const vector<int> v which is the input set of numbers of size N
We'll use foo in the inner_product, initializing it with decreasing powers of 10:
generate(begin(foo), end(foo), [i=int{1}]() mutable {
auto result = i;
i *= 10;
return result; });
We can then use this in a loop:
auto maximum = 0;
for (auto it = prev(rend(v), size(foo) + 1); it != rbegin(v); advance(it, -1)) {
maximum = max<int>(inner_product(cbegin(foo), cend(foo), it, 0), maximum);
}
maximum = max<int>(inner_product(cbegin(foo), cend(foo), rbegin(v), 0), maximum);
Live Example
To use it's initialization requires that your initial M was smaller than N, so you may want to assert that or something.
--EDITED--
here's my suggestion with STACK based on my previous suggestion using vector
findMaxValueOutOfNDigits(stackInput, M, N)
{
// stackInput = [2, 9, 3, 6, 5, 8, 8, 8, 8, 7, 2, 2, 8, 1, 4]
// *where 4 was the first element to be inserted and 2 was the last to be inserted
// if the sequence is inverted, you can quickly fix it by doing a "for x = 0; x < stack.length; x++ { newStack.push(stack.pop()) }"
currentMaxValue = 0
for i = 0; i < (M - N + 1); i++
{
tempValue = process(stackInput, M, N)
stackInput.pop()
if (tempValue > currentMaxValue)
currentMaxValue = tempValue
}
return currentMaxValue
}
process(stackInput, M, N)
{
tempValue = stackInput.pop() * 10^(N - 1)
*howManyItemsCanILook = (M - N + 1)
for y = (N - 2); y == 0; y++
{
currentHowManyItemsCanILook = *howManyItemsCanILook
tempValue = tempValue + getValue(stackInput, *howManyItemsCanILook) * 10^(y)
*howManyItemsCanILook = *howManyItemsCanILook - 1
for x = 0; x < (currentHowManyItemsCanILook - *howManyItemsCanILook); x++
{
stackInput.pop()
}
}
return tempValue
}
getValue(stackInput, *howManyItemsCanILook)
{
currentMaxValue = stackInput.pop()
if (currentMaxValue == 9)
return 9
else
{
goUntil = *howManyItemsCanILook
for i = 0; i < goUntil; i++
{
*howManyItemsCanILook = *howManyItemsCanILook - 1
tempValue = stackInput.pop()
if (currentMaxValue < tempValue)
{
currentMaxValue = tempValue
if (currentMaxValue == 9)
return currentMaxValue
}
}
return currentMaxValue
}
}
note: where *howManyItemsCanILook is passed by reference
I hope this helps

How to reduce complexity of this code

Please can any one provide with a better algorithm then trying all the combinations for this problem.
Given an array A of N numbers, find the number of distinct pairs (i,
j) such that j >=i and A[i] = A[j].
First line of the input contains number of test cases T. Each test
case has two lines, first line is the number N, followed by a line
consisting of N integers which are the elements of array A.
For each test case print the number of distinct pairs.
Constraints:
1 <= T <= 10
1 <= N <= 10^6
-10^6 <= A[i] <= 10^6 for 0 <= i < N
I think that first sorting the array then finding frequency of every distinct integer and then adding nC2 of all the frequencies plus adding the length of the string at last. But unfortunately it gives wrong ans for some cases which are not known help. here is the implementation.
code:
#include <iostream>
#include<cstdio>
#include<algorithm>
using namespace std;
long fun(long a) //to find the aC2 for given a
{
if (a == 1) return 0;
return (a * (a - 1)) / 2;
}
int main()
{
long t, i, j, n, tmp = 0;
long long count;
long ar[1000000];
cin >> t;
while (t--)
{
cin >> n;
for (i = 0; i < n; i++)
{
cin >> ar[i];
}
count = 0;
sort(ar, ar + n);
for (i = 0; i < n - 1; i++)
{
if (ar[i] == ar[i + 1])
{
tmp++;
}
else
{
count += fun(tmp + 1);
tmp = 0;
}
}
if (tmp != 0)
{
count += fun(tmp + 1);
}
cout << count + n << "\n";
}
return 0;
}
Keep a count of how many times each number appears in an array. Then iterate over the result array and add the triangular number for each.
For example(from the source test case):
Input:
3
1 2 1
count array = {0, 2, 1} // no zeroes, two ones, one two
pairs = triangle(0) + triangle(2) + triangle(1)
pairs = 0 + 3 + 1
pairs = 4
Triangle numbers can be computed by (n * n + n) / 2, and the whole thing is O(n).
Edit:
First, there's no need to sort if you're counting frequency. I see what you did with sorting, but if you just keep a separate array of frequencies, it's easier. It takes more space, but since the elements and array length are both restrained to < 10^6, the max you'll need is an int[10^6]. This easily fits in the 256MB space requirements given in the challenge. (whoops, since elements can go negative, you'll need an array twice that size. still well under the limit, though)
For the n choose 2 part, the part you had wrong is that it's an n+1 choose 2 problem. Since you can pair each one by itself, you have to add one to n. I know you were adding n at the end, but it's not the same. The difference between tri(n) and tri(n+1) is not one, but n.

Triangle numbers problem....show within 4 seconds

The sequence of triangle numbers is
generated by adding the natural
numbers. So the 7th triangle number
would be 1 + 2 + 3 + 4 + 5 + 6 + 7 =
28. The first ten terms would be:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55,
...
Let us list the factors of the first
seven triangle numbers:
1: 1
3: 1,3
6: 1,2,3,6
10: 1,2,5,10
15: 1,3,5,15
21: 1,3,7,21
28: 1,2,4,7,14,28
We can see that 28 is the first
triangle number to have over five
divisors.
Given an integer n, display the first
triangle number having at least n
divisors.
Sample Input: 5
Output 28
Input Constraints: 1<=n<=320
I was obviously able to do this question, but I used a naive algorithm:
Get n.
Find triangle numbers and check their number of factors using the mod operator.
But the challenge was to show the output within 4 seconds of input. On high inputs like 190 and above it took almost 15-16 seconds. Then I tried to put the triangle numbers and their number of factors in a 2d array first and then get the input from the user and search the array. But somehow I couldn't do it: I got a lot of processor faults. Please try doing it with this method and paste the code. Or if there are any better ways, please tell me.
Here's a hint:
The number of divisors according to the Divisor function is the product of the power of each prime factor plus 1. For example, let's consider the exponential prime representation of 28:
28 = 22 * 30 * 50 * 71 * 110...
The product of each exponent plus one is: (2+1)*(0+1)*(0+1)*(1+1)*(0+1)... = 6, and sure enough, 28 has 6 divisors.
Now, consider that the nth triangular number can be computed in closed form as n(n+1)/2. We can multiply numbers written in the exponential prime form simply by adding up the exponents at each position. Dividing by two just means decrementing the exponent on the two's place.
Do you see where I'm going with this?
Well, you don't go into a lot of detail about what you did, but I can give you an optimization that can be used, if you didn't think of it...
If you're using the straightforward method of trying to find factors of a number n, by using the mod operator, you don't need to check all the numbers < n. That obviously would take n comparisons...you can just go up to floor(sqrt(n)). For each factor you find, just divide n by that number, and you'll get the conjugate value, and not need to find it manually.
For example: say n is 15.
We loop, and try 1 first. Yep, the mod checks out, so it's a factor. We divide n by the factor to get the conjugate value, so we do (15 / 1) = 15...so 15 is a factor.
We try 2 next. Nope. Then 3. Yep, which also gives us (15 / 3) = 5.
And we're done, because 4 is > floor(sqrt(n)). Quick!
If you didn't think of it, that might be something you could leverage to improve your times...overall you go from O(n) to O(sqrt (n)) which is pretty good (though for numbers this small, constants may still weigh heavily.)
I was in a programming competition way back in school where there was some similar question with a run time limit. the team that "solved" it did as follows:
1) solve it with a brute force slow method.
2) write a program to just print out the answer (you found using the slow method), which will run sub second.
I thought this was bogus, but they won.
see Triangular numbers: a(n) = C(n+1,2) = n(n+1)/2 = 0+1+2+...+n. (Formerly M2535 N1002)
then pick the language you want implement it in, see this:
"... Python
import math
def diminishing_returns(val, scale):
if val < 0:
return -diminishing_returns(-val, scale)
mult = val / float(scale)
trinum = (math.sqrt(8.0 * mult + 1.0) - 1.0) / 2.0
return trinum * scale
..."
First, create table with two columns: Triangle_Number Count_of_Factors.
Second, derive from this a table with the same columns, but consisting only of the 320 rows of the lowest triangle number with a distinct number of factors.
Perform your speedy lookup to the second table.
If you solved the problem, you should be able to access the thread on Project Euler in which people post their (some very efficient) solutions.
If you're going to copy and paste a problem, please cite the source (unless it was your teacher who stole it); and I second Wouter van Niferick's comment.
Well, at least you got a good professor. Performance is important.
Since you have a program that can do the job, you can precalculate all of the answers for 1 .. 320.
Store them in an array, then simply subscript into the array to get the answer. That will be very fast.
Compile with care, winner of worst code of the year :D
#include <iostream>
bool isPrime( unsigned long long number ){
if( number != 2 && number % 2 == 0 )
return false;
for( int i = 3;
i < static_cast<unsigned long long>
( sqrt(static_cast<double>(number)) + 1 )
; i += 2 ){
if( number % i == 0 )
return false;
}
return true;
}
unsigned int p;
unsigned long long primes[1024];
void initPrimes(){
primes[0] = 2;
primes[1] = 3;
unsigned long long number = 5;
for( unsigned int i = 2; i < 1024; i++ ){
while( !isPrime(number) )
number += 2;
primes[i] = number;
number += 2;
}
return;
}
unsigned long long nextPrime(){
unsigned int ret = p;
p++;
return primes[ret];
}
unsigned long long numOfDivs( unsigned long long number ){
p = 0;
std::vector<unsigned long long> v;
unsigned long long prime = nextPrime(), divs = 1, i = 0;
while( number >= prime ){
i = 0;
while( number % prime == 0 ){
number /= prime;
i++;
}
if( i )
v.push_back( i );
prime = nextPrime();
}
for( unsigned n = 0; n < v.size(); n++ )
divs *= (v[n] + 1);
return divs;
}
unsigned long long nextTriNumber(){
static unsigned long long triNumber = 1, next = 2;
unsigned long long retTri = triNumber;
triNumber += next;
next++;
return retTri;
}
int main()
{
initPrimes();
unsigned long long n = nextTriNumber();
unsigned long long divs = 500;
while( numOfDivs(n) <= divs )
n = nextTriNumber();
std::cout << n;
std::cin.get();
}
def first_triangle_number_with_over_N_divisors(N):
n = 4
primes = [2, 3]
fact = [None, None, {2:1}, {3:1}]
def num_divisors (x):
num = 1
for mul in fact[x].values():
num *= (mul+1)
return num
while True:
factn = {}
for p in primes:
if p > n//2: break
r = n // p
if r * p == n:
factn = fact[r].copy()
factn[p] = factn.get(p,0) + 1
if len(factn)==0:
primes.append(n)
factn[n] = 1
fact.append(factn)
(x, y) = (n-1, n//2) if n % 2 == 0 else (n, (n-1)//2)
numdiv = num_divisors(x) * num_divisors(y)
if numdiv >= N:
print('Triangle number %d: %d divisors'
%(x*y, numdiv))
break
n += 1
>>> first_triangle_number_with_over_N_divisors(500)
Triangle number 76576500: 576 divisors
Dude here is ur code, go have a look. It calculates the first number that has divisors greater than 500.
void main() {
long long divisors = 0;
long long nat_num = 0;
long long tri_num = 0;
int tri_sqrt = 0;
while (1) {
divisors = 0;
nat_num++;
tri_num = nat_num + tri_num;
tri_sqrt = floor(sqrt((double)tri_num));
long long i = 0;
for ( i=tri_sqrt; i>=1; i--) {
long long remainder = tri_num % i;
if ( remainder == 0 && tri_num == 1 ) {
divisors++;
}
else if (remainder == 0 && tri_num != 1) {
divisors++;
divisors++;
}
}
if (divisors >100) {
cout <<"No. of divisors: "<<divisors<<endl<<tri_num<<endl;
}
if (divisors > 500)
break;
}
cout<<"Final Result: "<<tri_num<<endl;
system("pause");
}
Boojum's answer motivated me to write this little program. It seems to work well, although it does use a brute force method of computing primes. It's neat how all the natural numbers can be broken down into prime number components.
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <iomanip>
#include <vector>
//////////////////////////////////////////////////////////////////////////////
typedef std::vector<size_t> uint_vector;
//////////////////////////////////////////////////////////////////////////////
// add a prime number to primes[]
void
primeAdd(uint_vector& primes)
{
size_t n;
if (primes.empty())
{
primes.push_back(2);
return;
}
for (n = *(--primes.end()) + 1; ; ++n)
{
// n is even -> not prime
if ((n & 1) == 0) continue;
// look for a divisor in [2,n)
for (size_t i = 2; i < n; ++i)
{
if ((n % i) == 0) continue;
}
// found a prime
break;
}
primes.push_back(n);
}
//////////////////////////////////////////////////////////////////////////////
void
primeFactorize(size_t n, uint_vector& primes, uint_vector& f)
{
f.clear();
for (size_t i = 0; n > 1; ++i)
{
while (primes.size() <= i) primeAdd(primes);
while (f.size() <= i) f.push_back(0);
while ((n % primes[i]) == 0)
{
++f[i];
n /= primes[i];
}
}
}
//////////////////////////////////////////////////////////////////////////////
int
main(int argc, char** argv)
{
// allow specifying number of TN's to be evaluated
size_t lim = 1000;
if (argc > 1)
{
lim = atoi(argv[1]);
}
if (lim == 0) lim = 1000;
// prime numbers
uint_vector primes;
// factors of (n), (n + 1)
uint_vector* f = new uint_vector();
uint_vector* f1 = new uint_vector();
// sum vector
uint_vector sum;
// prime factorize (n)
size_t n = 1;
primeFactorize(n, primes, *f);
// iterate over triangle-numbers
for (; n <= lim; ++n)
{
// prime factorize (n + 1)
primeFactorize(n + 1, primes, *f1);
while (f->size() < f1->size()) f->push_back(0);
while (f1->size() < f->size()) f1->push_back(0);
size_t numTerms = f->size();
// compute prime factors for (n * (n + 1) / 2)
sum.clear();
size_t i;
for (i = 0; i < numTerms; ++i)
{
sum.push_back((*f)[i] + (*f1)[i]);
}
--sum[0];
size_t numFactors = 1, tn = 1;
for (i = 0; i < numTerms; ++i)
{
size_t exp = sum[i];
numFactors *= (exp + 1);
while (exp-- != 0) tn *= primes[i];
}
std::cout
<< n << ". Triangle number "
<< tn << " has " << numFactors << " factors."
<< std::endl;
// prepare for next iteration
f->clear();
uint_vector* tmp = f;
f = f1;
f1 = tmp;
}
delete f;
delete f1;
return 0;
}