my teacher gave me this :
n<=10^6;
an array of n integer :ai..an(ai<=10^9);
find all prime numbers .
he said something about sieve of eratosthenes,and I read about it,also the wheel factorization too,but I still couldn't figure it out how to get the program (fpc) to run in 1s.??
as I know it's impossible,but still want to know your opinion .
and with the wheel factorization ,a 2*3 circle will treat 25 as a prime number,and I wanna ask if there is a way to find out the first number of the wheel treated wrong as a prime number.
example:2*3*5 circle ,how to find the first composite number treated as aprime number??
please help..and sorry for bad english.
A proper Sieve of Eratosthenes should find the primes less than a billion in about a second; it's possible. If you show us your code, we'll be happy to help you find what is wrong.
The smallest composite not marked by a 2,3,5-wheel is 49: the next largest prime not a member of the wheel is 7, and 7 * 7 = 49.
I did it now and it's finding primes up to 1000000 in a few milliseconds, without displaying all those numbers.
Declare an array a of n + 1 bools (if it is zero-based). At the beginning 0th and 1st element are false, all others are true (false is not a prime).
The algorithm looks like that:
i = 2;
while i * i <= n
if a[i] == true
j = i * i;
while j < n
a[j] = false;
j = j + i;
i = i + 1;
In a loop the condition is i * i <= n because you start searching from i * i (smaller primes than that was found already by one of other primes) so square root of i must not be bigger than n. You remove all numbers which are multiplies of primes up to n.
Time complexity is O(n log log n).
If you want to display primes, you display indexes which values in array are true.
Factorization is usefull if you want to find e.g. all semiprimes from 0 to n (products of two prime numbers). Then you find all smallest prime divisors from 0 to n/2 and check for each number if it has prime divisor and if number divided by its prime divisor has zero divisors. If so - it is a semiprime. My program wrote like that was calculating 8 times faster than first finding all primes and then multiplying them and saving result in an array.
Related
I have been solving a problem but then got stuck upon its subpart which is as follows:
Given an array of N elements whose ith element is A[i] and we are given Q queries of the type [L,R].
For each query output the number of divisors of product from Lth element to Rth element.
More formally, for each query lets define P as P = A[L] * A[L+1] * A[L+2] * ...* A[R].
Output the number of divisors of P modulo 998244353.
Constraints : 1<= N,Q <= 100000, 1<= A[i] <= 1000000.
My Approach,
For each index i, I have defined a map< int, int > which stores the prime divisor and its count in the product up to [1, i].
I am extracting the prime divisors of a number in O(LogN) using Sieve.
Then for each query (lets say {L,R} ), I am iterating through the map of Lth element and subtracting the count of each each key from the map of Rth element.
And then I am answering the query using the result:
if N = a^p * b^q * c^r ...(a,b,c being primes)
the number of divisors = (p+1)(q+1)(r+1)..
The time complexity of above solution is O(ND + QD), where D = number of distinct prime numbers upto 1000000. In worst case D = 78498.
Is there more efficient solution than this?
There is a more efficient solution for this. But it is slightly complicated. Here are steps to get to the necessary data structure.
Define a data type prime_factor that is a struct that contains a prime and a count.
Define a data type prime_factorization that is a vector of the first data type in ascending size of the primes. This can store the factorization of a number.
Write a function that takes a number, and turns its prime factorization into a prime_factorization
Write a function that takes 2 prime_factorization vectors and merges them into the factorization of the product of the two.
For each number in your array, compute its prime factorization. That gets stored in an array.
For each pair in your array, compute the prime factorization of the product. We will only need half of them. So elements 0, 1 go into one factorization, 2, 3 into the next and so on.
Repeat step 6 O(log(N)) times. So you have a vector of the factorization of each number, pairs, fours, eights, and so on. This results in approximately 2N precomputed factorization vectors. Most vectors are small though a few can be up to O(D) in size (where D is the number of distinct primes). Most of the merges should be very, very fast.
And now you have all of your data prepared. It can't take more than O(log(N)) times the space that storing the prime factors required by itself. (Less than that normally, though, because repeats among the small primes get gathered together in one prime_factor.)
Any range is the union of at most O(log(N)) of these computed vectors. For example the range 10..25 can be broken up into 10..11, 12..15, 16..24, 25. Arrange these intervals from smallest to largest and merge them. Then compute your answer from the result.
An exact analysis is complicated. But I assure you that query time is bounded above by O(Q * D * log(N)) and normally is much less than that.
UPDATE:
How do you find those intervals?
The answer is that you need to identify the number divisible by the highest power of 2 in the range, and then fill out both sides from there. And you figure that out by dividing by 2 (rounding down) until the range is of length 1. Then multiply the top boundary by 2 to find that mid-point.
For example if your range was 35-53 you would start by dividing by 2 to get 35-53, 17-26, 8-13, 4-6, 2-3. That was 2^4 we divided by. our power of 2 mid-point is 3*2^4 = 48. Our intervals above that midpoint are then 48-52, 53-53. Our intervals below are 40-47, 36-39, 35-35. And each of them is of length a power of 2 and starts at a number divisible by that power of 2.
First off all, feel free to improve the formatting of the question
This question is already solved by Goswin von Brederlow!
Hello, i'm praticing programming and this problem came across and i don't know how is the best way to solve this:
The question have T test cases that consists in:
Given a range L and R, find how many numbers meet the constraints:
i) Number is a perfect square;
ii) The sqrt(Number) is a prime number.
Limits:
1 <= T <= 1e4
1 <= L, R <= 1e12
Time limit: 1 second
So, i tried it with a simple idea of pre-processing with an unodered_map(lld, bool) all the answers with the sieve of eratosthenes for each sqrt(Number) given that Number is a perfect square
After, I pass in range pow(sqrt(l), 2) and increments it the next odd number... like: 4 9 16 25, the difference are odd numbers: 5 7 9...
My code:
long long int l, r; scanf("%lld %lld", &l, &r);
long long int odd = ceil(sqrt(l)), n = odd*odd;
odd = (2*odd) + 1;
long long int ans = 0;
while((n >= l && n <= r)){
it = h.find(n);
ans = ans + it->second;
n = n + odd;
odd = odd + 2;
}
But a i still got TLE, i need some help guys, thank you!
The only question I see I infer from "i don't know how is the best way to solve this".
Your way seems to be pretty smart already. You have some bugs even in just the bit of code you pasted. You seem to compute the sum of the numbers instead of counting them.
One thing I could think of improving is the counting itself. Looks like you have a hash table of all the squares of primes and you simply test all auqares if they are in the table.
Why not make a balanced tree out of the primes? In each node you store the minimum and maximum number and the count for the subtree. The leaves would be (n, n, 1) where n is one of the squares of primes. Given L and R you can then go through the tree and sum up all subtrees that are in the interval and recurse into subtrees that are only partially inside. Ignore everything outside. That should add a logarithmic factor to your complexity.
I'm currently solving a problem which is pretty straightforward: I need to find all prime numbers up to N which contain 101 in them and count them.
Say if N is 1000 then the output should ne 6 since there is 101, 1013, 1015, 5101, 6101 and 8101. I used sieve's algorithm to get all of the prime numbers up to N, though I don't know how could I solve it completely. I thought of std::find, but resigned from that idea because the time complexity grows fast. I know I need to modify sieve's algorithm to fit my needs though I can't find any patterns.
Any help would be appreciated.
Edit:
I'm using this algorithm:
vector<int> sieve;
vector<int> primes;
for (int i = 1; i < max + 1; ++i)
sieve.push_back(i); // you'll learn more efficient ways to handle this later
sieve[0]=0;
for (int i = 2; i < max + 1; ++i) { // there are lots of brace styles, this is mine
if (sieve[i-1] != 0) {
primes.push_back(sieve[i-1]);
for (int j = 2 * sieve[i-1]; j < max + 1; j += sieve[i-1]) {
sieve[j-1] = 0;
}
}
}
Yes, checking every prime number for containing "101" is a waste of time. Generating all numbers containing 101 and checking whether they are prime is probably faster.
For generating the 101 numbers, let's look at the possible digit patterns, e.g. with 5 digits:
nn101
n101m
101mm
For each of these patterns, you get all the numbers by iterating n in an outer loop and m in an inner loop and doing the math to get the pattern's value (of course, you need not consider even m values, because the only even prime is 2). You are done when the values reaches N.
To check for being a prime, an easy way is to prepare a list of all primes up to M=sqrt(N), using a sieve if you like, and check whether your value is divisible by one of them.
This should be running in O(N^1.5). Why? The number of patterns grows with O(logN), the iterations inside each pattern with N/1000, giving O(N), the prime check with the number of primes up to M, being O(M/log(M)), finding these primes with a sieve being O(M^2). Altogether that's O(N * log(N) * sqrt(N) / log(sqrt(N)) + N) or O(N^1.5).
You don't need to modify your prime generation algorithm. When processing the primes you get from your gen alg you just have to check if the prime satisfies your condition:
e.g.
// p is the prime number
if (contains(p))
{
// print or write to file or whatever
}
with:
bool contains(int);
a function which checks your condition
Inputs are two values 1 <= m , n <= 10^12
i don't know why my code is taking soo long for large values . time limit is 1 sec. please suggest me some critical modifications.
#include<iostream>
#include<algorithm>
using namespace std;
int main()
{
unsigned long long m,n,count=0;
cin >> m >> n;
for (long long int i = 1; i <= ((min(m,n))/2)+1; i++) //i divided min(m,n) by 2 to make it efficient.
{
if ((m%i == 0) && (n%i == 0))
{
count++;
}
}
if (((n%m == 0) || (m%n == 0)) && (n!=m))
{
cout << count << endl;
}
printf("%lld",count); //cout<<count;
system("pause");
return 0;
}
Firstly
((min(m, n)) / 2) + 1
Is being calculated every iteration. But it's loop-invariant. In general loop invariant code can be calculated before the loop, and stored. It will add up, but there are obviously much better ways to improve things. I'll describe one below:
you can make this much faster by calculating how many common prime factors there are, and by dividing out any "found" primes as you go. e.g. if only one number is divisible by 5, and the other is not, you can divide that one by 5 and you still get the same answer for common factors. Divide m and n by any "found" numbers as you go through it. (but keep checking whether either is divisible by e.g. 2 and keep dividing before you go on).
e.g. if the two numbers are both divisible by 2, 3 and 5, then the number of ways those three primes can combine is 8 (2^3), treating the presence of each prime as a true/false thing. So each prime that occurs once multiplies the number of combos by 2.
If any of the primes occurs more than once, then it changes the equation slightly. e.g. if the two numbers are divisible by 4, 3, 5:
4 = 2^2, so you could have no "2s", 1 "2" or 2 "2s" in the combined factor, so the total combinations 3 x 2 x 2 = 12. So any prime that occurs "x" times, multiplies the total number of combos by "x+1".
So basically, you don't need to check for every actual factor, you just need to search for how many common prime factors there are, then work out how many combos that adds up to. Luckily you only need to store one value, "total_combos" and multiply it by the "x+1" value for each found number as you go.
And a handy thing is that you can divide out all primes as they're found, and you're guaranteed that the largest remaining prime to be found is no larger than the square root of the smallest remaining number out of m and n.
So to run you through how this would work, start with a copy of m and n, loop up to the sqrt of the min of those two (m and n will be reduced as the loop cycles through).
make a value "total_combos", which starts at 1.
Check for 2's first, find out how many common powers of 2 there are, add one to that number. Divide out ALL the 2's from m and n, even if they're not matched, because reducing down the number cuts the total amount you actually need to search. You count the 2's, add one, then multiply "total_combos" by that. Keep dividing m or n by two as long as either has a factor of 2 remaining.
Then check for 3's, find out how many common powers of 3 there are, add one, the multiply "total_combos" by that. Divide out any and all factors of 3 when you're doing this.
then check for 4's. Since 4 isn't prime and we got rid of all 2's already, there will be zero 4's. Add one to that = 1, then we times "total_combos" by 1, so it stays the same. We didn't need to check whether 4 was prime or not, the divisions we already did ensured it's ignored. Same for any power of 2.
then check for 5's. same deal as 2's and 3's. And so on. All the prime bases get divided out as you go, so whenever a value actually matches you can be sure it's a new prime.
stop the loop when it exceeds sqrt(max(m,n)) (EDITED: min is probably wrong there). But m and n here are the values that have had all the lower primes divided out, so it's much faster.
I hope this approach is helpful.
There is a better way to solve this problem.
All you have to do is take the GCD of two numbers. Now any number won't divide m & n if they are greater than their GCD. So all you to do is that run a loop till the i<=Math.sqrt(GCD(m,n)) and check if the m%i==0 and n%i==0 only. It will save a lot of nanosecs.
An array of integers A[i] (i > 1) is defined in the following way: an element A[k] ( k > 1) is the smallest number greater than A[k-1] such that the sum of its digits is equal to the sum of the digits of the number 4* A[k-1] .
You need to write a program that calculates the N th number in this array based on the given first element A[1] .
INPUT:
In one line of standard input there are two numbers seperated with a single space: A[1] (1 <= A[1] <= 100) and N (1 <= N <= 10000).
OUTPUT:
The standard output should only contain a single integer A[N] , the Nth number of the defined sequence.
Input:
7 4
Output:
79
Explanation:
Elements of the array are as follows: 7, 19, 49, 79... and the 4th element is solution.
I tried solving this by coding a separate function that for a given number A[k] calculates the sum of it's digits and finds the smallest number greater than A[k-1] as it says in the problem, but with no success. The first testing failed because of a memory limit, the second testing failed because of a time limit, and now i don't have any possible idea how to solve this. One friend suggested recursion, but i don't know how to set that.
Anyone who can help me in any way please write, also suggest some ideas about using recursion/DP for solving this problem. Thanks.
This has nothing to do with recursion and almost nothing with dynamic programming. You just need to find viable optimizations to make it fast enough. Just a hint, try to understand this solution:
http://codepad.org/LkTJEILz
Here is a simple solution in python. It only uses iteration, recursion is unnecessary and inefficient even for a quick and dirty solution.
def sumDigits(x):
sum = 0;
while(x>0):
sum += x % 10
x /= 10
return sum
def homework(a0, N):
a = [a0]
while(len(a) < N):
nextNum = a[len(a)-1] + 1
while(sumDigits(nextNum) != sumDigits(4 * a[len(a)-1])):
nextNum += 1
a.append(nextNum)
return a[N-1]
PS. I know we're not really supposed to give homework answers, but it appears the OP is in an intro to C++ class so probably doesn't know python yet, hopefully it just looks like pseudo code. Also the code is missing many simple optimizations which would probably make it too slow for a solution as is.
It is rather recursive.
The kernel of the problem is:
Find the smallest number N greater than K having digitsum(N) = J.
If digitsum(K) == J then test if N = K + 9 satisfies the condition.
If digitsum(K) < J then possibly N differs from K only in the ones digit (if the digitsum can be achieved without exceeding 9).
Otherwise if digitsum(K) <= J the new ones digit is 9 and the problem recurses to "Find the smallest number N' greater than (K/10) having digitsum(N') = J-9, then N = N'*10 + 9".
If digitsum(K) > J then ???
In every case N <= 4 * K
9 -> 18 by the first rule
52 -> 55 by the second rule
99 -> 189 by the third rule, the first rule is used during recursion
25 -> 100 requires the fourth case, which I had originally not seen the need for.
Any more counterexamples?