I am trying to solve this: SPOJ problem.
And after some research I found out that it comes down to a simple calculation of the nth fib number, however n can get really large so an O(n) solution won't do any good. Googling around, I found that you can calculate the nth fib number in O(logn) and also a code sample that does exactly that:
long long fibonacci(int n) {
long long fib[2][2] = {{1,1},{1,0}}, ret[2][2] = {{1,0},{0,1}}, tmp[2][2] = {{0,0},{0,0}};
int i, j, k;
while (n) {
if (n & 1) {
memset(tmp, 0, sizeof tmp);
for (i = 0; i < 2; i++)
for (j = 0; j < 2; j++)
for (k = 0; k < 2; k++)
tmp[i][j] = (tmp[i][j] + ret[i][k] * fib[k][j]);
for (i = 0; i < 2; i++)
for (j = 0; j < 2; j++)
ret[i][j] = tmp[i][j];
}
memset(tmp, 0, sizeof tmp);
for (i = 0; i < 2; i++)
for (j = 0; j < 2; j++)
for (k = 0; k < 2; k++)
tmp[i][j] = (tmp[i][j] + fib[i][k] * fib[k][j]);
for (i = 0; i < 2; i++)
for (j = 0; j < 2; j++)
fib[i][j] = tmp[i][j];
n /= 2;
}
return (ret[0][1]);
}
I tried to modify it for the problem and am still getting WA: http://ideone.com/3TtE5m
Am I calculating the modular arithmetic wrong? Or is something else the issue?
You mean the nth Fibonacci number I hope.
In order to do it you need a matrix decomposition of Fibonacci numbers described here.
The basic idea is you take the Donald E. Knuth matrix identity form for a Fibonacci number which is:
And instead of calculating the Fibonacci numbers in the traditional way you will try and find the matrix to the power of (k) where k is the given number.
So this is solving the problem in k matrix multiplications, not really helpful since we can do it in much easier way.
But wait! We can optimise the matrix multiplication. Instead of doing the k multiplications we can square it first and then do the half of the multiplications. And we can keep on doing it. So if the given number is 2a then we can do it in a steps. By keeping squaring the matrix.
If the number is not a power of 2 we can do the binary decomposition of a number and see whether to take the given squared matrix into final product or not.
In your case after each multiplication you also need to apply modulo operator 123456 to each matrix element.
Hope my explanation helps if not see the link for a clearer and longer one.
There is actually one more caveat of the task: as you are asked to provide some Fibonacci number modulo a given number, you should also prove that taking the remainder of each matrix element doesn't change the result. In other words if we multiply matrices and take remainder that we are actually still getting the Fibonacci number remainders. But since the remainder operation is distributive in addition and multiplication it actually does produce the correct results.
The Fibonacci numbers occur as the ratio of successive convergents of the continued fraction for , and the matrix formed from successive convergents of any continued fraction has a determinant of +1 or −1.
The matrix representation gives the following closed-form expression for the Fibonacci numbers i.e.
The matrix is multiplied n time because then only we can get the (n+1)th Fibonacci number as the element at the row and the column (0, 0) in the resultant matrix.
If we apply the above method without using recursive matrix multiplication, then the Time Complexity: O(n) and Space Complexity: O(1).
But we want Time Complexity: O(log n), so we have to optimize the above method, and this can be done by recursive multiplication of matrix to get the nth power.
Implementation of the above rule can be found below.
#include <stdio.h>
void multiply(int F[2][2], int M[2][2]);
void power(int F[2][2], int n);
/*
The function that returns nth Fibonacci number.
*/
int fib(int n) {
int F[2][2] = {{1, 1}, {1, 0}};
if (n == 0)
return 0;
power(F, n - 1);
return F[0][0];
}
/*
Optimized using recursive multiplication.
*/
void power(int F[2][2], int n) {
if ( n == 0 || n == 1)
return;
int M[2][2] = {{1, 1}, {1, 0}};
power(F, n / 2);
multiply(F, F);
if (n % 2 != 0)
multiply(F, M);
}
void multiply(int F[2][2], int M[2][2]) {
int x = F[0][0] * M[0][0] + F[0][1] * M[1][0];
int y = F[0][0] * M[0][1] + F[0][1] * M[1][1];
int z = F[1][0] * M[0][0] + F[1][1] * M[1][0];
int w = F[1][0] * M[0][1] + F[1][1] * M[1][1];
F[0][0] = x;
F[0][1] = y;
F[1][0] = z;
F[1][1] = w;
}
int main() {
printf("%d\n", fib(15));
/*
15th Fibonacci number is 610.
*/
return 0;
}
There is a very simple algorithm, using only integers:
long long fib(int n) {
long long a, b, p, q;
a = q = 1;
b = p = 0;
while (n > 0) {
if (n % 2 == 0) {
long long qq = q*q;
q = 2*p*q + qq;
p = p*p + qq;
n /= 2;
} else {
long long aq = a*q;
a = b*q + aq + a*p;
b = b*p + aq;
n -= 1;
}
}
return b;
}
This is based on the identities of the Lucas sequence.
Related
I'm trying to write a C program which performs multiplication of two numbers without directly using the multiplication operator, and it should take into account numbers which are sufficiently large so that even the usual addition of these two numbers cannot be performed by direct addition.
I was motivated for this when I was trying to (and successfully did) write a C program which performs addition using character strings, I did the following:
#include<stdio.h>
#define N 100000
#include<string.h>
void pushelts(char X[], int n){
int i, j;
for (j = 0; j < n; j++){
for (i = strlen(X); i >= 0; i--){
X[i + 1] = X[i];
}
X[0] = '0';
}
}
int max(int a, int b){
if (a > b){ return a; }
return b;
}
void main(){
char E[N], F[N]; int C[N]; int i, j, a, b, c, d = 0, e;
printf("Enter the first number: ");
gets_s(E);
printf("\nEnter the second number: ");
gets_s(F);
a = strlen(E); b = strlen(F); c = max(a, b);
pushelts(E, c - a); pushelts(F, c - b);
for (i = c - 1; i >= 0; i--){
e = d + E[i] + F[i] - 2*'0';
C[i] = e % 10; d = e / 10;
}
printf("\nThe answer is: ");
for (i = 0; i < c; i++){
printf("%d", C[i]);
}
getchar();
}
It can add any two numbers with "N" digits. Now, how would I use this to perform multiplication of large numbers? First, I wrote a function which performs the multiplication of number, which is to be entered as a string of characters, by a digit n (i.e. 0 <= n <= 9). It's easy to see how such a function is written; I'll call it (*). Now the main purpose is to multiply two numbers (entered as a string of characters) with each other. We might look at the second number with k digits (assuming it's a1a2.....ak) as:
a1a2...ak = a1 x 10^(k - 1) + a2 x 10^(k - 2) + ... + ak-1 x 10 + ak
So the multiplication of the two numbers can be achieved using the solution designed for addition and the function (*).
If the first number is x1x2.....xn and the second one is y1y2....yk, then:
x1x2...xn x y1y2...yk = (x1x2...xn) x y1 x 10^(k-1) + .....
Now the function (*) can multiply (x1x2...xn) with y1 and the multiplication by 10^(k-1) is just adding k-1 zero's next to the number; finally we add all of these k terms with each other to obtain the result. But the difficulty lies in just knowing how many digits each number contains in order to perform the addition each time inside the loop designed for adding them together. I have thought about doing a null array and each time adding to it the obtained result from multiplication of (x1x2....xn) by yi x 10^(i-1), but like I've said I am incapable of precising the required bounds and I don't know how many zeros I should each time add in front of each obtained result in order to add it using the above algorithm to the null array. More difficulty arises when I'll have to do several conversions from char types into int types and conversely. Maybe I'm making this more complicated than it should; I don't know if there's an easier way to do this or if there are tools I'm unaware of. I'm a beginner at programming and I don't know further than the elementary tools.
Does anyone have a solution or an idea or an algorithm to present? Thanks.
There is an algorithm for this which I developed when doing Small Factorials problem on SPOJ.
This algorithm is based on the elementary school multiplication method. In school days we learn multiplication of two numbers by multiplying each digit of the first number with the last digit of the second number. Then multiplying each digit of the first number with second last digit of the second number and so on as follows:
1234
x 56
------------
7404
+6170- // - is denoting the left shift
------------
69104
What actually is happening:
num1 = 1234, num2 = 56, left_shift = 0;
char_array[] = all digits in num1
result_array[]
while(num2)
n = num2%10
num2 /= 10
carry = 0, i = left_shift, j = 0
while(char_array[j])
i. partial_result = char_array[j]*n + carry
ii. partial_result += result_array[i]
iii. result_array[i++] = partial_result%10
iv. carry = partial_result/10
left_shift++
Print the result_array in reverse order.
You should note that the above algorithm work if num1 and num2 do not exceed the range of its data type. If you want more generic program, then you have to read both numbers in char arrays. Logic will be the same. Declare num1 and num2 as char array. See the implementation:
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
int main(void)
{
char num1[200], num2[200];
char result_arr[400] = {'\0'};
int left_shift = 0;
fgets(num1, 200, stdin);
fgets(num2, 200, stdin);
size_t n1 = strlen(num1);
size_t n2 = strlen(num2);
for(size_t i = n2-2; i >= 0; i--)
{
int carry = 0, k = left_shift;
for(size_t j = n1-2; j >= 0; j--)
{
int partial_result = (num1[j] - '0')*(num2[i] - '0') + carry;
if(result_arr[k])
partial_result += result_arr[k] - '0';
result_arr[k++] = partial_result%10 + '0';
carry = partial_result/10;
}
if(carry > 0)
result_arr[k] = carry +'0';
left_shift++;
}
//printf("%s\n", result_arr);
size_t len = strlen(result_arr);
for(size_t i = len-1; i >= 0; i-- )
printf("%c", result_arr[i]);
printf("\n");
}
This is not a standard algorithm but I hope this will help.
Bignum arithmetic is hard to implement efficiently. The algorithms are quite hard to understand (and efficient algorithms are better than the naive one you are trying to implement), and you could find several books on them.
I would suggest using an existing Bignum library like GMPLib or use some language providing bignums natively (e.g. Common Lisp with SBCL)
You could re-use your character-string-addition code as follows (using user300234's example of 384 x 56):
Set result="0" /* using your character-string representation */
repeat:
Set N = ones_digit_of_multiplier /* 6 in this case */
for (i = 0; i < N; ++i)
result += multiplicand /* using your addition algorithm */
Append "0" to multiplicand /* multiply it by 10 --> 3840 */
Chop off the bottom digit of multiplier /* divide it by 10 --> 5 */
Repeat if multiplier != 0.
Could you explain me how the following two algorithms work?
int countSort(int arr[], int n, int exp)
{
int output[n];
int i, count[n] ;
for (int i=0; i < n; i++)
count[i] = 0;
for (i = 0; i < n; i++)
count[ (arr[i]/exp)%n ]++;
for (i = 1; i < n; i++)
count[i] += count[i - 1];
for (i = n - 1; i >= 0; i--)
{
output[count[ (arr[i]/exp)%n] - 1] = arr[i];
count[(arr[i]/exp)%n]--;
}
for (i = 0; i < n; i++)
arr[i] = output[i];
}
void sort(int arr[], int n)
{
countSort(arr, n, 1);
countSort(arr, n, n);
}
I wanted to apply the algorithm at this array:
After calling the function countSort(arr, n, 1) , we get this:
When I call then the function countSort(arr, n, n) , at this for loop:
for (i = n - 1; i >= 0; i--)
{
output[count[ (arr[i]/exp)%n] - 1] = arr[i];
count[(arr[i]/exp)%n]--;
}
I get output[-1]=arr[4].
But the array doesn't have such a position...
Have I done something wrong?
EDIT:Considering the array arr[] = { 10, 6, 8, 2, 3 }, the array count will contain the following elements:
what do these numbers represent? How do we use them?
Counting sort is very easy - let's say you have an array which contains numbers from range 1..3:
[3,1,2,3,1,1,3,1,2]
You can count how many times each number occurs in the array:
count[1] = 4
count[2] = 2
count[3] = 3
Now you know that in a sorted array,
number 1 will occupy positions 0..3 (from 0 to count[1] - 1), followed by
number 2 on positions 4..5 (from count[1] to count[1] + count[2] - 1), followed by
number 3 on positions 6..8 (from count[1] + count[2] to count[1] + count[2] + count[3] - 1).
Now that you know final position of every number, you can just insert every number at its correct position. That's basically what countSort function does.
However, in real life your input array would not contain just numbers from range 1..3, so the solution is to sort numbers on the least significant digit (LSD) first, then LSD-1 ... up to the most significant digit.
This way you can sort bigger numbers by sorting numbers from range 0..9 (single digit range in decimal numeral system).
This code: (arr[i]/exp)%n in countSort is used just to get those digits. n is base of your numeral system, so for decimal you should use n = 10 and exp should start with 1 and be multiplied by base in every iteration to get consecutive digits.
For example, if we want to get third digit from right side, we use n = 10 and exp = 10^2:
x = 1234,
(x/exp)%n = 2.
This algorithm is called Radix sort and is explained in detail on Wikipedia: http://en.wikipedia.org/wiki/Radix_sort
It took a bit of time to pick though your countSort routine and attempt to determine just what it was you were doing compared to a normal radix sort. There are some versions that split the iteration and the actual sort routine which appears to be what you attempted using both countSort and sort functions. However, after going though that exercise, it was clear you had just missed including necessary parts of the sort routine. After fixing various compile/declaration issues in your original code, the following adds the pieces you overlooked.
In your countSort function, the size of your count array was wrong. It must be the size of the base, in this case 10. (you had 5) You confused the use of exp and base throughout the function. The exp variable steps through the powers of 10 allowing you to get the value and position of each element in the array when combined with a modulo base operation. You had modulo n instead. This problem also permeated you loop ranges, where you had a number of your loop indexes iterating over 0 < n where the correct range was 0 < base.
You missed finding the maximum value in the original array which is then used to limit the number of passes through the array to perform the sort. In fact all of your existing loops in countSort must fall within the outer-loop iterating while (m / exp > 0). Lastly, you omitted a increment of exp within the outer-loop necessary to applying the sort to each element within the array. I guess you just got confused, but I commend your effort in attempting to rewrite the sort routine and not just copy/pasting from somewhere else. (you may have copied/pasted, but if that's the case, you have additional problems...)
With each of those issues addressed, the sort works. Look though the changes and understand what it is doing. The radix sort/count sort are distribution sorts relying on where numbers occur and manipulating indexes rather than comparing values against one another which makes this type of sort awkward to understand at first. Let me know if you have any questions. I made attempts to preserve your naming convention throughout the function, with the addition of a couple that were omitted and to prevent hardcoding 10 as the base.
#include <stdio.h>
void prnarray (int *a, int sz);
void countSort (int arr[], int n, int base)
{
int exp = 1;
int m = arr[0];
int output[n];
int count[base];
int i;
for (i = 1; i < n; i++) /* find the maximum value */
m = (arr[i] > m) ? arr[i] : m;
while (m / exp > 0)
{
for (i = 0; i < base; i++)
count[i] = 0; /* zero bucket array (count) */
for (i = 0; i < n; i++)
count[ (arr[i]/exp) % base ]++; /* count keys to go in each bucket */
for (i = 1; i < base; i++) /* indexes after end of each bucket */
count[i] += count[i - 1];
for (i = n - 1; i >= 0; i--) /* map bucket indexes to keys */
{
output[count[ (arr[i]/exp) % base] - 1] = arr[i];
count[(arr[i]/exp)%n]--;
}
for (i = 0; i < n; i++) /* fill array with sorted output */
arr[i] = output[i];
exp *= base; /* inc exp for next group of keys */
}
}
int main (void) {
int arr[] = { 10, 6, 8, 2, 3 };
int n = 5;
int base = 10;
printf ("\n The original array is:\n\n");
prnarray (arr, n);
countSort (arr, n, base);
printf ("\n The sorted array is\n\n");
prnarray (arr, n);
printf ("\n");
return 0;
}
void prnarray (int *a, int sz)
{
register int i;
printf (" [");
for (i = 0; i < sz; i++)
printf (" %d", a[i]);
printf (" ]\n");
}
output:
$ ./bin/sort_count
The original array is:
[ 10 6 8 2 3 ]
The sorted array is
[ 2 3 6 8 10 ]
I'm trying to write the first 10 terms of the Fibonacci sequence. I feel like I'm on the right line, but I can't seem to quite grasp the actual code (in C).
float fib = 0;
const float minn = 1;
const float maxn = 20;
float n = minn;
while (n <= maxn);{
n = n + 1;
printf (" %4,2f", fib);
fib = (n - 1) + (n - 2);
}
With the fibonacci sequence the value f(n) = f(n - 1) + f(n = 2). the first three values are defined as 0, 1, 1.
The fibonacci sequence is a sequence of integer values (math integers, not necessarily C language values). consider using int or long for the fibonacci value. float is worthless, it only adds unneeded overhead.
when calculating the fibonacci sequence you must store the previous 2 values to get the next value.
you want 10 fibonacci values. you know the first three already so print those and then calculate the next seven values.
7 values implies a loop that iterates 7 times. it has no bearing on the maximum value of the fibonacci value returned, just how many values you want to print.
do something like this:
printf("0, 1, 1");
int currentValue;
int valueN1 = 1;
int valueN2 = 1;
for (int counter = 1; counter <= 7; ++counter)
{
currentValue = valueN1 + valueN2;
printf(", %d", currentValue);
valueN2 = valueN1;
valueN1 = currentValue;
}
You need run loop 10 times only,to find first 10 terms of the Fibonacci sequence.
in your code,while loop would not let you go further because of semicolon at the end of loop
//declare fib value as long int or unsigned int
// because the value of any fib term is not at all
long int fib;
int n=1;
while (n <= 10)
{
printf (" %d", fib);
fib = fib_term(n);
n = n + 1;
}
implement fib_term(int n); by seeing this snippet
First off, I would suggest changing your datatype from a float to an integer or other datatype. floats are not exact numbers and if you had used while (n = maxn) instead of while (n <= maxn) you could have ended up with an infinite loap since the two floats would never have matched.
Second, you don't seem to really understand what the fibonacci sequence is. Take a look at the wikipedie article http://en.wikipedia.org/wiki/Fibonacci_number.
The fibinocci number is NOT (n - 1) + (n - 2) like you have. It is the sum of the previous two numbers in the sequence. You need to restructure your loop to hold the last two values and calculate the next one based on these values.
There are (at least) 2 ways to implement the Fibonacci Algorithm in C:
The Iterative:
int fib(int n){
if (n == 0)
return 0;
int a = 1
int b = 1;
for (int i = 3; i <= n; i++) {
int c = a + b;
a = b;
b = c;
}
return b;
}
The Recursive:
unsigned int fibonacci_recursive(unsigned int n)
{
if (n == 0)
{
return 0;
}
if (n == 1) {
return 1;
}
return fibonacci_recursive(n - 1) + fibonacci_recursive(n - 2);
}
void main(){
unsigned int i = fibonacci_recursive(10);
}
Suggestions
Consider integer types before FP types when doing integer problems.
Omit a ; in your while (n <= maxn);{
Use a . in floating point formats %4.2f instead of %4,2f.
Fibonacci is the sum of the previous 2 terms, not simply fib = (n - 1) + (n - 2).
Consider an unsigned solution:
C code:
void Fibonacci_Sequence(unsigned n) {
const unsigned minn = 1;
const unsigned maxn = 20;
unsigned F[3];
F[0] = 0;
F[1] = 1;
unsigned i = 0;
for (i = 0; i <= maxn; i++) {
if (i >= minn) printf(" %u,", F[0]);
F[2] = F[1] + F[0];
F[0] = F[1];
F[1] = F[2];
}
}
This uses n/2 iterations
#include<stdio.h>
main()
{
int i,n,a=0,b=1,odd;
scanf("%d",&n);
odd=n%2;
for(i=1;i<=n/2;i++)
{
printf("%d %d ",a,b);
a=a+b;
b=a+b;
}
if(odd)
printf("%d",a);
}
Approach 1:
C(n,r) = n!/(n-r)!r!
Approach 2:
In the book Combinatorial Algorithms by wilf, i have found this:
C(n,r) can be written as C(n-1,r) + C(n-1,r-1).
e.g.
C(7,4) = C(6,4) + C(6,3)
= C(5,4) + C(5,3) + C(5,3) + C(5,2)
. .
. .
. .
. .
After solving
= C(4,4) + C(4,1) + 3*C(3,3) + 3*C(3,1) + 6*C(2,1) + 6*C(2,2)
As you can see, the final solution doesn't need any multiplication. In every form C(n,r), either n==r or r==1.
Here is the sample code i have implemented:
int foo(int n,int r)
{
if(n==r) return 1;
if(r==1) return n;
return foo(n-1,r) + foo(n-1,r-1);
}
See output here.
In the approach 2, there are overlapping sub-problems where we are calling recursion to solve the same sub-problems again. We can avoid it by using Dynamic Programming.
I want to know which is the better way to calculate C(n,r)?.
Both approaches will save time, but the first one is very prone to integer overflow.
Approach 1:
This approach will generate result in shortest time (in at most n/2 iterations), and the possibility of overflow can be reduced by doing the multiplications carefully:
long long C(int n, int r) {
if(r > n - r) r = n - r; // because C(n, r) == C(n, n - r)
long long ans = 1;
int i;
for(i = 1; i <= r; i++) {
ans *= n - r + i;
ans /= i;
}
return ans;
}
This code will start multiplication of the numerator from the smaller end, and as the product of any k consecutive integers is divisible by k!, there will be no divisibility problem. But the possibility of overflow is still there, another useful trick may be dividing n - r + i and i by their GCD before doing the multiplication and division (and still overflow may occur).
Approach 2:
In this approach, you'll be actually building up the Pascal's Triangle. The dynamic approach is much faster than the recursive one (the first one is O(n^2) while the other is exponential). However, you'll need to use O(n^2) memory too.
# define MAX 100 // assuming we need first 100 rows
long long triangle[MAX + 1][MAX + 1];
void makeTriangle() {
int i, j;
// initialize the first row
triangle[0][0] = 1; // C(0, 0) = 1
for(i = 1; i < MAX; i++) {
triangle[i][0] = 1; // C(i, 0) = 1
for(j = 1; j <= i; j++) {
triangle[i][j] = triangle[i - 1][j - 1] + triangle[i - 1][j];
}
}
}
long long C(int n, int r) {
return triangle[n][r];
}
Then you can look up any C(n, r) in O(1) time.
If you need a particular C(n, r) (i.e. the full triangle is not needed), then the memory consumption can be made O(n) by overwriting the same row of the triangle, top to bottom.
# define MAX 100
long long row[MAX + 1];
int C(int n, int r) {
int i, j;
// initialize by the first row
row[0] = 1; // this is the value of C(0, 0)
for(i = 1; i <= n; i++) {
for(j = i; j > 0; j--) {
// from the recurrence C(n, r) = C(n - 1, r - 1) + C(n - 1, r)
row[j] += row[j - 1];
}
}
return row[r];
}
The inner loop is started from the end to simplify the calculations. If you start it from index 0, you'll need another variable to store the value being overwritten.
I think your recursive approach should work efficiently with DP. But it will start giving problems once the constraints increase. See http://www.spoj.pl/problems/MARBLES/
Here is the function which i use in online judges and coding contests. So it works quite fast.
long combi(int n,int k)
{
long ans=1;
k=k>n-k?n-k:k;
int j=1;
for(;j<=k;j++,n--)
{
if(n%j==0)
{
ans*=n/j;
}else
if(ans%j==0)
{
ans=ans/j*n;
}else
{
ans=(ans*n)/j;
}
}
return ans;
}
It is an efficient implementation for your Approach #1
Your Recursive Approach is fine but using DP with your approach will reduce the overhead of solving subproblems again.Now since we already have two Conditions-
nCr(n,r) = nCr(n-1,r-1) + nCr(n-1,r);
nCr(n,0)=nCr(n,n)=1;
Now we can easily build a DP solution by storing our subresults in a 2-D array-
int dp[max][max];
//Initialise array elements with zero
int nCr(int n, int r)
{
if(n==r) return dp[n][r] = 1; //Base Case
if(r==0) return dp[n][r] = 1; //Base Case
if(r==1) return dp[n][r] = n;
if(dp[n][r]) return dp[n][r]; // Using Subproblem Result
return dp[n][r] = nCr(n-1,r) + nCr(n-1,r-1);
}
Now if you want to further otimise, Getting the prime factorization of the binomial coefficient is probably the most efficient way to calculate it, especially if multiplication is expensive.
The fastest method I know is Vladimir's method. One avoids division all together by decomposing nCr into prime factors. As Vladimir says you can do this pretty efficiently using Eratosthenes sieve.Also,Use Fermat's little theorem to calculate nCr mod MOD(Where MOD is a prime number).
Using dynamic programming you can easily find the nCr here is the solution
package com.practice.competitive.maths;
import java.util.Scanner;
public class NCR1 {
public static void main(String[] args) {
try (Scanner scanner = new Scanner(System.in)) {
int testCase = scanner.nextInt();
while (testCase-- > 0) {
int n = scanner.nextInt();
int r = scanner.nextInt();
int[][] combination = combination();
System.out.println(combination[n][r]%1000000007);
}
} catch (Exception e) {
e.printStackTrace();
}
}
public static int[][] combination() {
int combination[][] = new int[1001][1001];
for (int i = 0; i < 1001; i++)
for (int j = 0; j <= i; j++) {
if (j == 0 || j == i)
combination[i][j] = 1;
else
combination[i][j] = combination[i - 1][j - 1] % 1000000007 + combination[i - 1][j] % 1000000007;
}
return combination;
}
}
unsigned long long ans = 1,a=1,b=1;
int k = r,i=0;
if (r > (n-r))
k = n-r;
for (i = n ; k >=1 ; k--,i--)
{
a *= i;
b *= k;
if (a%b == 0)
{
a = (a/b);
b=1;
}
}
ans = a/b;
Given a snipplet of code, how will you determine the complexities in general. I find myself getting very confused with Big O questions. For example, a very simple question:
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
System.out.println("*");
}
}
The TA explained this with something like combinations. Like this is n choose 2 = (n(n-1))/2 = n^2 + 0.5, then remove the constant so it becomes n^2. I can put int test values and try but how does this combination thing come in?
What if theres an if statement? How is the complexity determined?
for (int i = 0; i < n; i++) {
if (i % 2 ==0) {
for (int j = i; j < n; j++) { ... }
} else {
for (int j = 0; j < i; j++) { ... }
}
}
Then what about recursion ...
int fib(int a, int b, int n) {
if (n == 3) {
return a + b;
} else {
return fib(b, a+b, n-1);
}
}
In general, there is no way to determine the complexity of a given function
Warning! Wall of text incoming!
1. There are very simple algorithms that no one knows whether they even halt or not.
There is no algorithm that can decide whether a given program halts or not, if given a certain input. Calculating the computational complexity is an even harder problem since not only do we need to prove that the algorithm halts but we need to prove how fast it does so.
//The Collatz conjecture states that the sequence generated by the following
// algorithm always reaches 1, for any initial positive integer. It has been
// an open problem for 70+ years now.
function col(n){
if (n == 1){
return 0;
}else if (n % 2 == 0){ //even
return 1 + col(n/2);
}else{ //odd
return 1 + col(3*n + 1);
}
}
2. Some algorithms have weird and off-beat complexities
A general "complexity determining scheme" would easily get too complicated because of these guys
//The Ackermann function. One of the first examples of a non-primitive-recursive algorithm.
function ack(m, n){
if(m == 0){
return n + 1;
}else if( n == 0 ){
return ack(m-1, 1);
}else{
return ack(m-1, ack(m, n-1));
}
}
function f(n){ return ack(n, n); }
//f(1) = 3
//f(2) = 7
//f(3) = 61
//f(4) takes longer then your wildest dreams to terminate.
3. Some functions are very simple but will confuse lots of kinds of static analysis attempts
//Mc'Carthy's 91 function. Try guessing what it does without
// running it or reading the Wikipedia page ;)
function f91(n){
if(n > 100){
return n - 10;
}else{
return f91(f91(n + 11));
}
}
That said, we still need a way to find the complexity of stuff, right? For loops are a simple and common pattern. Take your initial example:
for(i=0; i<N; i++){
for(j=0; j<i; j++){
print something
}
}
Since each print something is O(1), the time complexity of the algorithm will be determined by how many times we run that line. Well, as your TA mentioned, we do this by looking at the combinations in this case. The inner loop will run (N + (N-1) + ... + 1) times, for a total of (N+1)*N/2.
Since we disregard constants we get O(N2).
Now for the more tricky cases we can get more mathematical. Try to create a function whose value represents how long the algorithm takes to run, given the size N of the input. Often we can construct a recursive version of this function directly from the algorithm itself and so calculating the complexity becomes the problem of putting bounds on that function. We call this function a recurrence
For example:
function fib_like(n){
if(n <= 1){
return 17;
}else{
return 42 + fib_like(n-1) + fib_like(n-2);
}
}
it is easy to see that the running time, in terms of N, will be given by
T(N) = 1 if (N <= 1)
T(N) = T(N-1) + T(N-2) otherwise
Well, T(N) is just the good-old Fibonacci function. We can use induction to put some bounds on that.
For, example, Lets prove, by induction, that T(N) <= 2^n for all N (ie, T(N) is O(2^n))
base case: n = 0 or n = 1
T(0) = 1 <= 1 = 2^0
T(1) = 1 <= 2 = 2^1
inductive case (n > 1):
T(N) = T(n-1) + T(n-2)
aplying the inductive hypothesis in T(n-1) and T(n-2)...
T(N) <= 2^(n-1) + 2^(n-2)
so..
T(N) <= 2^(n-1) + 2^(n-1)
<= 2^n
(we can try doing something similar to prove the lower bound too)
In most cases, having a good guess on the final runtime of the function will allow you to easily solve recurrence problems with an induction proof. Of course, this requires you to be able to guess first - only lots of practice can help you here.
And as f final note, I would like to point out about the Master theorem, the only rule for more difficult recurrence problems I can think of now that is commonly used. Use it when you have to deal with a tricky divide and conquer algorithm.
Also, in your "if case" example, I would solve that by cheating and splitting it into two separate loops that don; t have an if inside.
for (int i = 0; i < n; i++) {
if (i % 2 ==0) {
for (int j = i; j < n; j++) { ... }
} else {
for (int j = 0; j < i; j++) { ... }
}
}
Has the same runtime as
for (int i = 0; i < n; i += 2) {
for (int j = i; j < n; j++) { ... }
}
for (int i = 1; i < n; i+=2) {
for (int j = 0; j < i; j++) { ... }
}
And each of the two parts can be easily seen to be O(N^2) for a total that is also O(N^2).
Note that I used a good trick trick to get rid of the "if" here. There is no general rule for doing so, as shown by the Collatz algorithm example
In general, deciding algorithm complexity is theoretically impossible.
However, one cool and code-centric method for doing it is to actually just think in terms of programs directly. Take your example:
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
System.out.println("*");
}
}
Now we want to analyze its complexity, so let's add a simple counter that counts the number of executions of the inner line:
int counter = 0;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
System.out.println("*");
counter++;
}
}
Because the System.out.println line doesn't really matter, let's remove it:
int counter = 0;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
counter++;
}
}
Now that we have only the counter left, we can obviously simplify the inner loop out:
int counter = 0;
for (int i = 0; i < n; i++) {
counter += n;
}
... because we know that the increment is run exactly n times. And now we see that counter is incremented by n exactly n times, so we simplify this to:
int counter = 0;
counter += n * n;
And we emerged with the (correct) O(n2) complexity :) It's there in the code :)
Let's look how this works for a recursive Fibonacci calculator:
int fib(int n) {
if (n < 2) return 1;
return fib(n - 1) + fib(n - 2);
}
Change the routine so that it returns the number of iterations spent inside it instead of the actual Fibonacci numbers:
int fib_count(int n) {
if (n < 2) return 1;
return fib_count(n - 1) + fib_count(n - 2);
}
It's still Fibonacci! :) So we know now that the recursive Fibonacci calculator is of complexity O(F(n)) where F is the Fibonacci number itself.
Ok, let's look at something more interesting, say simple (and inefficient) mergesort:
void mergesort(Array a, int from, int to) {
if (from >= to - 1) return;
int m = (from + to) / 2;
/* Recursively sort halves */
mergesort(a, from, m);
mergesort(m, m, to);
/* Then merge */
Array b = new Array(to - from);
int i = from;
int j = m;
int ptr = 0;
while (i < m || j < to) {
if (i == m || a[j] < a[i]) {
b[ptr] = a[j++];
} else {
b[ptr] = a[i++];
}
ptr++;
}
for (i = from; i < to; i++)
a[i] = b[i - from];
}
Because we are not interested in the actual result but the complexity, we change the routine so that it actually returns the number of units of work carried out:
int mergesort(Array a, int from, int to) {
if (from >= to - 1) return 1;
int m = (from + to) / 2;
/* Recursively sort halves */
int count = 0;
count += mergesort(a, from, m);
count += mergesort(m, m, to);
/* Then merge */
Array b = new Array(to - from);
int i = from;
int j = m;
int ptr = 0;
while (i < m || j < to) {
if (i == m || a[j] < a[i]) {
b[ptr] = a[j++];
} else {
b[ptr] = a[i++];
}
ptr++;
count++;
}
for (i = from; i < to; i++) {
count++;
a[i] = b[i - from];
}
return count;
}
Then we remove those lines that do not actually impact the counts and simplify:
int mergesort(Array a, int from, int to) {
if (from >= to - 1) return 1;
int m = (from + to) / 2;
/* Recursively sort halves */
int count = 0;
count += mergesort(a, from, m);
count += mergesort(m, m, to);
/* Then merge */
count += to - from;
/* Copy the array */
count += to - from;
return count;
}
Still simplifying a bit:
int mergesort(Array a, int from, int to) {
if (from >= to - 1) return 1;
int m = (from + to) / 2;
int count = 0;
count += mergesort(a, from, m);
count += mergesort(m, m, to);
count += (to - from) * 2;
return count;
}
We can now actually dispense with the array:
int mergesort(int from, int to) {
if (from >= to - 1) return 1;
int m = (from + to) / 2;
int count = 0;
count += mergesort(from, m);
count += mergesort(m, to);
count += (to - from) * 2;
return count;
}
We can now see that actually the absolute values of from and to do not matter any more, but only their distance, so we modify this to:
int mergesort(int d) {
if (d <= 1) return 1;
int count = 0;
count += mergesort(d / 2);
count += mergesort(d / 2);
count += d * 2;
return count;
}
And then we get to:
int mergesort(int d) {
if (d <= 1) return 1;
return 2 * mergesort(d / 2) + d * 2;
}
Here obviously d on the first call is the size of the array to be sorted, so you have the recurrence for the complexity M(x) (this is in plain sight on the second line :)
M(x) = 2(M(x/2) + x)
and this you need to solve in order to get to a closed form solution. This you do easiest by guessing the solution M(x) = x log x, and verify for the right side:
2 (x/2 log x/2 + x)
= x log x/2 + 2x
= x (log x - log 2 + 2)
= x (log x - C)
and verify it is asymptotically equivalent to the left side:
x log x - Cx
------------ = 1 - [Cx / (x log x)] = 1 - [C / log x] --> 1 - 0 = 1.
x log x
Even though this is an over generalization, I like to think of Big-O in terms of lists, where the length of the list is N items.
Thus, if you have a for-loop that iterates over everything in the list, it is O(N). In your code, you have one line that (in isolation all by itself) is 0(N).
for (int i = 0; i < n; i++) {
If you have a for loop nested inside another for loop, and you perform an operation on each item in the list that requires you to look at every item in the list, then you are doing an operation N times for each of N items, thus O(N^2). In your example above you do in fact, have another for loop nested inside your for loop. So you can think about it as if each for loop is 0(N), and then because they are nested, multiply them together for a total value of 0(N^2).
Conversely, if you are just doing a quick operation on a single item then that would be O(1). There is no 'list of length n' to go over, just a single one time operation.To put this in context, in your example above, the operation:
if (i % 2 ==0)
is 0(1). What is important isn't the 'if', but the fact that checking to see if a single item is equal to another item is a quick operation on a single item. Like before, the if statement is nested inside your external for loop. However, because it is 0(1), then you are multiplying everything by '1', and so there is no 'noticeable' affect in your final calculation for the run time of the entire function.
For logs, and dealing with more complex situations (like this business of counting up to j or i, and not just n again), I would point you towards a more elegant explanation here.
I like to use two things for Big-O notation: standard Big-O, which is worst case scenario, and average Big-O, which is what normally ends up happening. It also helps me to remember that Big-O notation is trying to approximate run-time as a function of N, the number of inputs.
The TA explained this with something like combinations. Like this is n choose 2 = (n(n-1))/2 = n^2 + 0.5, then remove the constant so it becomes n^2. I can put int test values and try but how does this combination thing come in?
As I said, normal big-O is worst case scenario. You can try to count the number of times that each line gets executed, but it is simpler to just look at the first example and say that there are two loops over the length of n, one embedded in the other, so it is n * n. If they were one after another, it'd be n + n, equaling 2n. Since its an approximation, you just say n or linear.
What if theres an if statement? How is the complexity determined?
This is where for me having average case and best case helps a lot for organizing my thoughts. In worst case, you ignore the if and say n^2. In average case, for your example, you have a loop over n, with another loop over part of n that happens half of the time. This gives you n * n/x/2 (the x is whatever fraction of n gets looped over in your embedded loops. This gives you n^2/(2x), so you'd get n^2 just the same. This is because its an approximation.
I know this isn't a complete answer to your question, but hopefully it sheds some kind of light on approximating complexities in code.
As has been said in the answers above mine, it is clearly not possible to determine this for all snippets of code; I just wanted to add the idea of using average case Big-O to the discussion.
For the first snippet, it's just n^2 because you perform n operations n times. If j was initialized to i, or went up to i, the explanation you posted would be more appropriate but as it stands it is not.
For the second snippet, you can easily see that half of the time the first one will be executed, and the second will be executed the other half of the time. Depending on what's in there (hopefully it's dependent on n), you can rewrite the equation as a recursive one.
The recursive equations (including the third snippet) can be written as such: the third one would appear as
T(n) = T(n-1) + 1
Which we can easily see is O(n).
Big-O is just an approximation, it doesn't say how long an algorithm takes to execute, it just says something about how much longer it takes when the size of its input grows.
So if the input is size N and the algorithm evaluates an expression of constant complexity: O(1) N times, the complexity of the algorithm is linear: O(N). If the expression has linear complexity, the algorithm has quadratic complexity: O(N*N).
Some expressions have exponential complexity: O(N^N) or logarithmic complexity: O(log N). For an algorithm with loops and recursion, multiply the complexities of each level of loop and/or recursion. In terms of complexity, looping and recursion are equivalent. An algorithm that has different complexities at different stages in the algorithm, choose the highest complexity and ignore the rest. And finally, all constant complexities are considered equivalent: O(5) is the same as O(1), O(5*N) is the same as O(N).