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#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <string.h>
#define PI 3.141592
void read_input(double *D, double *L, int *nx, double *t_F);
double main(void) {
/******************************/
/* Declarations of parameters */
/******************************/
/* Number of grid points */
int nx;
/* Length of domain */
double L;
/* Equation coefficients */
double D;
/* Length of time to run simulation. */
double t_F;
/* Read in from file; */
read_input(&D, &L, &nx, &t_F);
/* Grid spacing */
double dx = L/nx;
double invdx2 = 1.0/(dx*dx);
/* Time step */
double dt = 0.25/invdx2; // changed to 0.25/dx^2 to satisfy the stability condition
/************************************************/
/* Solution Storage at Current / Next time step */
/************************************************/
double *uc, *un, *vc, *vn;
/* Time splitting solutions */
double *uts1, *uts2, *vts1, *vts2;
/* Derivative used in finite difference */
double deriv;
/* Allocate memory according to size of nx */
uc = malloc(nx * sizeof(double));
un = malloc(nx * sizeof(double));
vc = malloc(nx * sizeof(double));
vn = malloc(nx * sizeof(double));
uts1 = malloc(nx * sizeof(double));
uts2 = malloc(nx * sizeof(double));
vts1 = malloc(nx * sizeof(double));
vts2 = malloc(nx * sizeof(double));
/* Check the allocation pointers */
if (uc==NULL||un==NULL||vc==NULL||vn==NULL||uts1==NULL||
uts2==NULL||vts1==NULL||vts2==NULL) {
printf("Memory allocation failed\n");
return 1;
}
int k;
double x;
/* Current time */
double ctime;
/* Initialise arrays */
for(k = 0; k < nx; k++) {
x = k*dx;
uc[k] = 1.0 + sin(2.0*PI*x/L);
vc[k] = 0.0;
/* Set other arrays to 0 */
uts1[k] = 0; uts2[k] = 0;
vts1[k] = 0; vts2[k] = 0;
}
/* Loop over timesteps */
while (ctime < t_F){
/* Rotation factors for time-splitting scheme. */
double cfac = cos(dt); //changed from 2*dt to dt
double sfac = sin(dt);
/* First substep for diffusion equation, A_1 */
for (k = 0; k < nx; k++) {
x = k*dx;
/* Diffusion at half time step. */
deriv = (uc[k-1] + uc[k+1] - 2*uc[k])*invdx2 ;
uts1[k] = uc[k] + (D * deriv + vc[k])* 0.5*dt; //
deriv = (vc[k-1] + vc[k+1] - 2*vc[k])*invdx2;
vts1[k] = vc[k] + (D * deriv - uc[k]) * 0.5*dt;
}
/* Second substep for decay/growth terms, A_2 */
for (k = 0; k < nx; k++) {
x = k*dx;
/* Apply rotation matrix to u and v, */
uts2[k] = cfac*uts1[k] + sfac*vts1[k];
vts2[k] = -sfac*uts1[k] + cfac*vts1[k];
}
/* Third substep for diffusion terms, A_1 */
for (k = 0; k < nx; k++) {
x = k*dx;
deriv = (uts2[k-1] + uts2[k+1] - 2*uts2[k])*invdx2;
un[k] = uts2[k] + (D * deriv + vts2[k]) * 0.5*dt;
deriv = (vts2[k-1] + vts2[k+1] - 2*vts2[k])*invdx2;
vn[k] = vts2[k] + (D * deriv - uts2[k]) * 0.5*dt;
}
/* Copy next values at timestep to u, v arrays. */
memcpy(uc,un, sizeof(double) * nx);
memcpy(vc,vn, sizeof(double) * nx);
/* Increment time. */
ctime += dt;
for (k = 0; k < nx; k++ ) {
x = k*dx;
printf("%g %g %g %g\n",ctime,x,uc[k],vc[k]);
}
}
/* Free allocated memory */
free(uc); free(un);
free(vc); free(vn);
free(uts1); free(uts2);
free(vts1); free(vts2);
return 0;
}
// The lines below don't contain any bugs! Don't modify them
void read_input(double *D, double *L, int *nx, double *t_F) {
FILE *infile;
if(!(infile=fopen("input.txt","r"))) {
printf("Error opening file\n");
exit(1);
}
if(4!=fscanf(infile,"%lf %lf %d %lf",D,L,nx,t_F)) {
printf("Error reading parameters from file\n");
exit(1);
}
fclose(infile);
}
So this is the code. It is meant to solve the following differential equations:
du/dt - Dd^2u/dx^2 - v = 0
dv/dt - Dd^2v/dx^2 + u = 0
It splits the equations into two parts. The second x derivative part(A1) and the decay part which contains u and v(A2) . It uses two half steps(0.5dt) for A1 and 1 full step of dt for A2. I know how to do time splitting but i dont know whether i have done it correctly here.
This is for an assignment and i have fixed all the errors and i am just trying to make the code work as intended. I have never had to solve something similar to this so i am definitely very stuck right now. The solution converges but i think its wrong. Any ideas why? Am not looking for someone to outright tell me what am doing wrong, just guide me in the right direction if you know what i mean.
PS: When i compile the code with gcc i get a warning about double main(void). Why might that be?
Let's say I've been given two integers a, b where a is a positive integer and is smaller than b. I have to find an efficient algorithm that's going to give me the sum of number of base2 digits (number of bits) over the interval [a, b]. For example, in the interval [0, 4] the sum of digits is equal to 9 because 0 = 1 digit, 1 = 1 digit, 2 = 2 digits, 3 = 2 digits and 4 = 3 digits.
My program is capable of calculating this number by using a loop but I'm looking for something more efficient for large numbers. Here are the snippets of my code just to give you an idea:
int numberOfBits(int i) {
if(i == 0) {
return 1;
}
else {
return (int) log2(i) + 1;
}
}
The function above is for calculating the number of digits of one number in the interval.
The code below shows you how I use it in my main function.
for(i = a; i <= b; i++) {
l = l + numberOfBits(i);
}
printf("Digits: %d\n", l);
Ideally I should be able to get the number of digits by using the two values of my interval and using some special algorithm to do that.
Try this code, i think it gives you what you are needing to calculate the binaries:
int bit(int x)
{
if(!x) return 1;
else
{
int i;
for(i = 0; x; i++, x >>= 1);
return i;
}
}
The main thing to understand here is that the number of digits used to represent a number in binary increases by one with each power of two:
+--------------+---------------+
| number range | binary digits |
+==============+===============+
| 0 - 1 | 1 |
+--------------+---------------+
| 2 - 3 | 2 |
+--------------+---------------+
| 4 - 7 | 3 |
+--------------+---------------+
| 8 - 15 | 4 |
+--------------+---------------+
| 16 - 31 | 5 |
+--------------+---------------+
| 32 - 63 | 6 |
+--------------+---------------+
| ... | ... |
A trivial improvement over your brute force algorithm would then be to figure out how many times this number of digits has increased between the two numbers passed in (given by the base two logarithm) and add up the digits by multiplying the count of numbers that can be represented by the given number of digits (given by the power of two) with the number of digits.
A naive implementation of this algorithm is:
int digits_sum_seq(int a, int b)
{
int sum = 0;
int i = 0;
int log2b = b <= 0 ? 1 : floor(log2(b));
int log2a = a <= 0 ? 1 : floor(log2(a)) + 1;
sum += (pow(2, log2a) - a) * (log2a);
for (i = log2b; i > log2a; i--)
sum += pow(2, i - 1) * i;
sum += (b - pow(2, log2b) + 1) * (log2b + 1);
return sum;
}
It can then be improved by the more efficient versions of the log and pow functions seen in the other answers.
First, we can improve the speed of log2, but that only gives us a fixed factor speed-up and doesn't change the scaling.
Faster log2 adapted from: https://graphics.stanford.edu/~seander/bithacks.html#IntegerLogLookup
The lookup table method takes only about 7 operations to find the log
of a 32-bit value. If extended for 64-bit quantities, it would take
roughly 9 operations. Another operation can be trimmed off by using
four tables, with the possible additions incorporated into each. Using
int table elements may be faster, depending on your architecture.
Second, we must re-think the algorithm. If you know that numbers between N and M have the same number of digits, would you add them up one by one or would you rather do (M-N+1)*numDigits?
But if we have a range where multiple numbers appear what do we do? Let's just find the intervals of same digits, and add sums of those intervals. Implemented below. I think that my findEndLimit could be further optimized with a lookup table.
Code
#include <stdio.h>
#include <limits.h>
#include <time.h>
unsigned int fastLog2(unsigned int v)
{
static const char LogTable256[256] =
{
#define LT(n) n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n
-1, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
LT(4), LT(5), LT(5), LT(6), LT(6), LT(6), LT(6),
LT(7), LT(7), LT(7), LT(7), LT(7), LT(7), LT(7), LT(7)
};
register unsigned int t, tt; // temporaries
if (tt = v >> 16)
{
return (t = tt >> 8) ? 24 + LogTable256[t] : 16 + LogTable256[tt];
}
else
{
return (t = v >> 8) ? 8 + LogTable256[t] : LogTable256[v];
}
}
unsigned int numberOfBits(unsigned int i)
{
if (i == 0) {
return 1;
}
else {
return fastLog2(i) + 1;
}
}
unsigned int findEndLimit(unsigned int sx, unsigned int ex)
{
unsigned int sy = numberOfBits(sx);
unsigned int ey = numberOfBits(ex);
unsigned int mx;
unsigned int my;
if (sy == ey) // this also means sx == ex
return ex;
// assumes sy < ey
mx = (ex - sx) / 2 + sx; // will eq. sx for sx + 1 == ex
my = numberOfBits(mx);
while (ex - sx != 1) {
mx = (ex - sx) / 2 + sx; // will eq. sx for sx + 1 == ex
my = numberOfBits(mx);
if (my == ey) {
ex = mx;
ey = numberOfBits(ex);
}
else {
sx = mx;
sy = numberOfBits(sx);
}
}
return sx+1;
}
int main(void)
{
unsigned int a, b, m;
unsigned long l;
clock_t start, end;
l = 0;
a = 0;
b = UINT_MAX;
start = clock();
unsigned int i;
for (i = a; i < b; ++i) {
l += numberOfBits(i);
}
if (i == b) {
l += numberOfBits(i);
}
end = clock();
printf("Naive\n");
printf("Digits: %ld; Time: %fs\n",l, ((double)(end-start))/CLOCKS_PER_SEC);
l=0;
start = clock();
do {
m = findEndLimit(a, b);
l += (b-m + 1) * (unsigned long)numberOfBits(b);
b = m-1;
} while (b > a);
l += (b-a+1) * (unsigned long)numberOfBits(b);
end = clock();
printf("Binary search\n");
printf("Digits: %ld; Time: %fs\n",l, ((double)(end-start))/CLOCKS_PER_SEC);
}
Output
From 0 to UINT_MAX
$ ./main
Naive
Digits: 133143986178; Time: 25.722492s
Binary search
Digits: 133143986178; Time: 0.000025s
My findEndLimit can take long time in some edge cases:
From UINT_MAX/16+1 to UINT_MAX/8
$ ./main
Naive
Digits: 7784628224; Time: 1.651067s
Binary search
Digits: 7784628224; Time: 4.921520s
Conceptually, you would need to split the task to two subproblems -
1) find the sum of digits from 0..M, and from 0..N, then subtract.
2) find the floor(log2(x)), because eg for the number 77 the numbers 64,65,...77 all have 6 digits, the next 32 have 5 digits, the next 16 have 4 digits and so on, which makes a geometric progression.
Thus:
int digits(int a) {
if (a == 0) return 1; // should digits(0) be 0 or 1 ?
int b=(int)floor(log2(a)); // use any all-integer calculation hack
int sum = 1 + (b+1) * (a- (1<<b) +1); // added 1, due to digits(0)==1
while (--b)
sum += (b + 1) << b; // shortcut for (b + 1) * (1 << b);
return sum;
}
int digits_range(int a, int b) {
if (a <= 0 || b <= 0) return -1; // formulas work for strictly positive numbers
return digits(b)-digits(a-1);
}
As efficiency depends on the tools available, one approach would be doing it "analog":
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
unsigned long long pow2sum_min(unsigned long long n, long long unsigned m)
{
if (m >= n)
{
return 1;
}
--n;
return (2ULL << n) + pow2sum_min(n, m);
}
#define LN(x) (log2(x)/log2(M_E))
int main(int argc, char** argv)
{
if (2 >= argc)
{
fprintf(stderr, "%s a b\n", argv[0]);
exit(EXIT_FAILURE);
}
long a = atol(argv[1]), b = atol(argv[2]);
if (0L >= a || 0L >= b || b < a)
{
puts("Na ...!");
exit(EXIT_FAILURE);
}
/* Expand intevall to cover full dimensions: */
unsigned long long a_c = pow(2, floor(log2(a)));
unsigned long long b_c = pow(2, floor(log2(b+1)) + 1);
double log2_a_c = log2(a_c);
double log2_b_c = log2(b_c);
unsigned long p2s = pow2sum_min(log2_b_c, log2_a_c) - 1;
/* Integral log2(x) between a_c and b_c: */
double A = ((b_c * (LN(b_c) - 1))
- (a_c * (LN(a_c) - 1)))/LN(2)
+ (b+1 - a);
/* "Integer"-integral - integral of log2(x)'s inverse function (2**x) between log(a_c) and log(b_c): */
double D = p2s - (b_c - a_c)/LN(2);
/* Corrective from a_c/b_c to a/b : */
double C = (log2_b_c - 1)*(b_c - (b+1)) + log2_a_c*(a - a_c);
printf("Total used digits: %lld\n", (long long) ((A - D - C) +.5));
}
:-)
The main thing here is the number and kind of iterations done.
Number is
log(floor(b_c)) - log(floor(a_c))
times
doing one
n - 1 /* Integer decrement */
2**n + s /* One bit-shift and one integer addition */
for each iteration.
Here's an entirely look-up based approach. You don't even need the log2 :)
Algorithm
First we precompute interval limits where the number of bits would change and create a lookup table. In other words we create an array limits[2^n], where limits[i] gives us the biggest integer that can be represented with (i+1) bits. Our array is then {1, 3, 7, ..., 2^n-1}.
Then, when we want to determine the sum of bits for our range, we must first match our range limits a and b with the smallest index for which a <= limits[i] and b <= limits[j] holds, which will then tell us that we need (i+1) bits to represent a, and (j+1) bits to represent b.
If the indexes are the same, then the result is simply (b-a+1)*(i+1), otherwise we must separately get the number of bits from our value to the edge of same number of bits interval, and add up total number of bits for each interval between as well. In any case, simple arithmetic.
Code
#include <stdio.h>
#include <limits.h>
#include <time.h>
unsigned long bitsnumsum(unsigned int a, unsigned int b)
{
// generate lookup table
// limits[i] is the max. number we can represent with (i+1) bits
static const unsigned int limits[32] =
{
#define LTN(n) n*2u-1, n*4u-1, n*8u-1, n*16u-1, n*32u-1, n*64u-1, n*128u-1, n*256u-1
LTN(1),
LTN(256),
LTN(256*256),
LTN(256*256*256)
};
// make it work for any order of arguments
if (b < a) {
unsigned int c = a;
a = b;
b = c;
}
// find interval of a
unsigned int i = 0;
while (a > limits[i]) {
++i;
}
// find interval of b
unsigned int j = i;
while (b > limits[j]) {
++j;
}
// add it all up
unsigned long sum = 0;
if (i == j) {
// a and b in the same range
// conveniently, this also deals with j == 0
// so no danger to do [j-1] below
return (i+1) * (unsigned long)(b - a + 1);
}
else {
// add sum of digits in range [a, limits[i]]
sum += (i+1) * (unsigned long)(limits[i] - a + 1);
// add sum of digits in range [limits[j], b]
sum += (j+1) * (unsigned long)(b - limits[j-1]);
// add sum of digits in range [limits[i], limits[j]]
for (++i; i<j; ++i) {
sum += (i+1) * (unsigned long)(limits[i] - limits[i-1]);
}
return sum;
}
}
int main(void)
{
clock_t start, end;
unsigned int a=0, b=UINT_MAX;
start = clock();
printf("Sum of binary digits for numbers in range "
"[%u, %u]: %lu\n", a, b, bitsnumsum(a, b));
end = clock();
printf("Time: %fs\n", ((double)(end-start))/CLOCKS_PER_SEC);
}
Output
$ ./lookup
Sum of binary digits for numbers in range [0, 4294967295]: 133143986178
Time: 0.000282s
Algorithm
The main idea is to find the n2 = log2(x) rounded down. That is the number of digits in x. Let pow2 = 1 << n2. n2 * (pow2 - x + 1) is the number of digits in the values [x...pow2]. Now find the sun of digits in the powers of 2 from 1 to n2-1
Code
I am certain various simplifications can be made.
Untested code. Will review later.
// Let us use unsigned for everything.
unsigned ulog2(unsigned value) {
unsigned result = 0;
if (0xFFFF0000u & value) {
value >>= 16; result += 16;
}
if (0xFF00u & value) {
value >>= 8; result += 8;
}
if (0xF0u & value) {
value >>= 4; result += 4;
}
if (0xCu & value) {
value >>= 2; result += 2;
}
if (0x2 & value) {
value >>= 1; result += 1;
}
return result;
}
unsigned bit_count_helper(unsigned x) {
if (x == 0) {
return 1;
}
unsigned n2 = ulog2(x);
unsigned pow2 = 1u << n;
unsigned sum = n2 * (pow2 - x + 1u); // value from pow2 to x
while (n2 > 0) {
// ... + 5*16 + 4*8 + 3*4 + 2*2 + 1*1
pow2 /= 2;
sum += n2 * pow2;
}
return sum;
}
unsigned bit_count(unsigned a, unsigned b) {
assert(a < b);
return bit_count_helper(b - 1) - bit_count_helper(a);
}
For this problem your solution is the simplest, the one called "naive" where you look for every element in the sequence or in your case interval for check something or execute operations.
Naive Algorithm
Assuming that a and b are positive integers with b greater than a let's call the dimension/size of the interval [a,b], n = (b-a).
Having our number of elements n and using some notations of algorithms (like big-O notation link), the worst case cost is O(n*(numberOfBits_cost)).
From this we can see that we can speed up our algorithm by using a faster algorithm for computing numberOfBits() or we need to find a way to not look at every element of the interval that costs us n operations.
Intuition
Now looking at a possible interval [6,14] you can see that for 6 and 7 we need 3 digits, with 4 need for 8,9,10,11,12,13,14. This results in calling numberOfBits() for every number that use the same number of digits to be represented, while the following multiplication operation would be faster:
(number_in_subinterval)*digitsForThisInterval
((14-8)+1)*4 = 28
((7-6)+1)*3 = 6
So we reduced the looping on 9 elements with 9 operations to only 2.
So writing a function that use this intuition will give us a more efficient in time, not necessarily in memory, algorithm. Using your numberOfBits() function I have created this solution:
int intuitionSol(int a, int b){
int digitsForA = numberOfBits(a);
int digitsForB = numberOfBits(b);
if(digitsForA != digitsForB){
//because a or b can be that isn't the first or last element of the
// interval that a specific number of digit can rappresent there is a need
// to execute some correction operation before on a and b
int tmp = pow(2,digitsForA) - a;
int result = tmp*digitsForA; //will containt the final result that will be returned
int i;
for(i = digitsForA + 1; i < digitsForB; i++){
int interval_elements = pow(2,i) - pow(2,i-1);
result = result + ((interval_elements) * i);
//printf("NumOfElem: %i for %i digits; sum:= %i\n", interval_elements, i, result);
}
int tmp1 = ((b + 1) - pow(2,digitsForB-1));
result = result + tmp1*digitsForB;
return result;
}
else {
int elements = (b - a) + 1;
return elements * digitsForA; // or digitsForB
}
}
Let's look at the cost, this algorithm costs is the cost of doing correction operation on a and b plus the most expensive one that of the for-loop. In my solution however I'm not looping over all elements but only on numberOfBits(b)-numberOfBits(a) that in the worst case, when [0,n], become log(n)-1 thats equivalent to O(log n).
To resume we passed from a linear operations cost O(n) to a logartmic one O(log n) in the worst case. Look on this diagram the diferinces between the two.
Note
When I talk about interval or sub-interval I refer to the interval of elements that use the same number of digits to represent the number in binary.
Following there are some output of my tests with the last one that shows the difference:
Considered interval is [0,4]
YourSol: 9 in time: 0.000015s
IntuitionSol: 9 in time: 0.000007s
Considered interval is [0,0]
YourSol: 1 in time: 0.000005s
IntuitionSol: 1 in time: 0.000005s
Considered interval is [4,7]
YourSol: 12 in time: 0.000016s
IntuitionSol: 12 in time: 0.000005s
Considered interval is [2,123456]
YourSol: 1967697 in time: 0.005010s
IntuitionSol: 1967697 in time: 0.000015s
First of all, let me tell you, I am learning programming.
Today, I tried to find the approximate value of cosine by using the taylor series. When I put n=0, my code gives me correct result of 1. But when I put n=1 or anything else, my code does not give correct result.
I am unable to understand where the problem is. Can anyone help?
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
int main(int argc, char *argv[])
{
float xnot = atof(argv[1]);
float n = atof(argv[2]);
float cosine = cos(xnot*(3.14159265/180));
float result;
printf("%.4f\n", cosine);
float min;
float d, c, b;
c = 1;
d = 2 * n;
for(b = 1; b <= d; b++){
c = c * b; /*value of the factorial is in c*/
}
c = c;
float power;
power = pow((-1), n);
xnot = pow(xnot, 2*n);
for(min = 0; min <= n; min++)
{
result += ((power * xnot) / c);
}
printf("%.4f", result);
}
When implementing the Taylor series you have to recompute the value of the terms for each value of 'n'. Here it looks like you've computed the value of -1^n (as xnot) for the maximum value of n and then you're just multiplying by that value for each iteration. That's wrong. Same for the values of x^2n / (2n)! - you have to recompute this for each value of n as you increment it, then sum up the values.
Best of luck.
You need to redo all calculations inside the for-loop. Keeping as much as your original code as possible, it could be something like:
int n = atoi(argv[2]); // n is an integer
...
...
float result = 1; // The first term (n=0) gives 1
for(int i = 1; i <= n; i++) // Start the loop from 1
{
float d, c, b;
c = 1;
d = 2 * i; // Use i instead of n
for(b = 1; b <= d; b++){
c = c * b; /*value of the factorial is in c*/
}
float power;
power = pow((-1), i); // Use i instead of n
xnot = pow(xnot, 2*i); // Use i instead of n
result += ((power * xnot) / c);
}
The code can be optimized - both for performance and precision - but as already stated I tried to keep it close to your original code.
When computing either sine or cosine with Taylor series, you also need to take the angular quadrants into consideration to minimize error growth. The following is a short example:
#define TSLIM 20 /* Series Limit (no. of terms) */
...
/** cos with taylor series expansion to n = TSLIM
* (no function reliance, quadrants handled)
*/
double cosenfq (const double deg)
{
double fp = deg - (int64_t)deg, /* save fractional part of deg */
qdeg = (int64_t)deg % 360, /* get equivalent 0-359 deg angle */
rad, cose_deg = 1.0; /* radians, cose_deg */
int pos_quad = 1, /* positive quadrant flag 1,4 */
sign = -1; /* taylor series term sign */
qdeg += fp; /* add fractional part back to angle */
/* get equivalent 0-90 degree angle, set pos_quad flag */
if (90 < qdeg && qdeg <= 180) { /* in 2nd quadrant */
qdeg = 180 - qdeg;
pos_quad = 0;
}
else if (180 < qdeg && qdeg <= 270) { /* in 3rd quadrant */
qdeg = qdeg - 180;
pos_quad = 0;
}
else if (270 < qdeg && qdeg <= 360) /* in 4th quadrant */
qdeg = 360 - qdeg;
rad = qdeg * M_PI / 180.0; /* convert to radians */
/* compute Taylor-Series expansion for sine for TSLIM / 2 terms */
for (int n = 2; n < TSLIM; n += 2, sign *= -1) {
double p = rad;
uint64_t f = n;
for (int i = 1; i < n; i++) /* pow */
p *= rad;
for (int i = 1; i < n; i++) /* nfact */
f *= i;
cose_deg += sign * p / f; /* Taylor-series term */
}
return pos_quad ? cose_deg : -cose_deg;
}
With a 20 term limit, max error is approximately 1.2E-15 (compared to math.h cos())
I am writing a program that for any given positive integers a < b < c will output YES if there is a solution to ax+by=c where x and y are also positive integers (x,y > 0), or NO if there isn't a solution. Keep in mind that I need to work with big numbers.
The approach I take for solving this problem is that I subtract b from c and I check if this number is divisable by a.
Here's my code:
#include <stdio.h>
#include <stdlib.h>
int main(){
unsigned long long int a, b, c;
scanf("%I64u %I64u %I64u", &a, &b, &c);
while(c>=a+b){ //if c becomes less than a+b, than there's no sollution
c-=b;
if(c%a==0){
printf("YES");
return 0;
}
}
printf("NO");
return 0;
}
is there a more optimised way to find wether ax+by=c has positive sollutions? I tried reading about linear Diophantine equations, but all I found is a way to find integer sollutions (but not positive).
My approach so far.
Use Euclidean Algorithm to find GCD(a, b)
There are solutions (in integers) to ax + by = c if and only if GCD(a, b) divides c. No integer solutions means no positive solutions.
use Extended Euclidean Algorithm to solve the Diophantine equation and return NO if it gives non-positive solutions.
For comparisons it's hard to find examples that take longer than a second but in deciding on thousands of random equations the performance difference is noticeable. This Lecture has a solution for finding the number of positive
solutions to a Linear Diophantine Equation.
typedef unsigned long long int BigInt;
int pos_solvable(BigInt a, BigInt b, BigInt c) {
/* returns 1 if there exists x, y > 0 s.t. ax + by = c
* where 0 < a < b < c
* returns 0, otherwise
*/
BigInt gcd = a, bb = b, temp;
while (bb) { /* Euclidean Algorithm */
temp = bb;
bb = gcd % bb;
gcd = temp;
}
if (c % gcd) { /* no integer (or positive) solution */
return 0;
} else {
/* Extended Euclidean Algorithm */
BigInt s = 0, old_s = 1;
BigInt t = 1, old_t = 0;
BigInt r = b / gcd, old_r = a / gcd;
while (r > 0) {
BigInt quotient = old_r / r;
BigInt ds = quotient * s;
BigInt dt = quotient * t;
if (ds > old_s || dt > old_t)
return 0; /* will give non-positive solution */
temp = s;
s = old_s - ds;
old_s = temp;
temp = t;
t = old_t - dt;
old_t = temp;
temp = r;
r = old_r - quotient * r;
old_r = temp;
}
return 1;
}
}
The following is a comment but too big for the comment section.
This is posted to help others dig into this problem a little deeper.
OP: Incorporate any of in your post if you like.
What is still needed are some challenging a,b,c.
#include <limits.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
//#define LLF "%I64u"
#define LLF "%llu"
int main(void) {
unsigned long long int a, b, c, x, y, sum, c0;
// scanf(LLF LLF LLF, &a, &b, &c);
c = c0 = ULLONG_MAX;
b = 10000223;
a = 10000169;
y = 0;
sum = a + b;
time_t t0 = time(NULL);
while (c >= sum) { //if c becomes less than a+b, than there's no solution
c -= b;
if (c % a == 0) {
break;
}
}
if (c % a == 0) {
y = (c0 - c) / b;
x = c / a;
printf("YES " LLF "*" LLF " + " LLF "*" LLF " = " LLF "\n", a, x, b, y, c);
} else {
printf("NO\n");
}
time_t t1 = time(NULL);
printf("time :" LLF "\n", (unsigned long long) (t1 - t0));
return 0;
}
Output
YES 10000169*1844638544065 + 10000223*4688810 = 18446697184563946985
time :0
I have been using Ubuntu 12.04 LTS with GCC to compile my the codes for my assignment for a while. However, recently I have run into two issues as follows:
The following code calculates zero for a nonzero value with the second formula is used.
There is a large amount of error in the calculation of the integral of the standard normal distribution from 0 to 5 or larger standard deviations.
How can I remedy these issues? I am especially obsessed with the first one. Any help or suggestion is appreciated. thanks in advance.
The code is as follows:
#include <stdio.h>
#include <math.h>
#include <limits.h>
#include <stdlib.h>
#define N 599
long double
factorial(long double n)
{
//Here s is the free parameter which is increased by one in each step and
//pro is the initial product and by setting pro to be 0 we also cover the
//case of zero factorial.
int s = 1;
long double pro = 1;
//Here pro stands for product.
if (n < 0)
printf("Factorial is not defined for a negative number \n");
else {
while (n >= s) {
pro *= s;
s++;
}
return pro;
}
}
int main()
{
// Since the function given is the standard normal distribution
// probability density function we have mean = 0 and variance = 1.
// Hence we also have z = x; while dealing with only positive values of
// x and keeping in mind that the PDF is symmetric around the mean.
long double * summand1 = malloc(N * sizeof(long double));
long double * summand2 = malloc(N * sizeof(long double));
int p = 0, k, z[5] = {0, 3, 5, 10, 20};
long double sum1[5] = {0}, sum2[5] = {0} , factor = 1.0;
for (p = 0; p <= 4; p++)
{
for (k = 0; k <= N; k++)
{
summand1[k] = (1 / sqrtl(M_PI * 2) )* powl(-1, k) * powl(z[p], 2 * k + 1) / ( factorial(k) * (2 * k + 1) * powl(2, k));
sum1[p] += summand1[k];
}
//Wolfamalpha site gives the same value here
for (k = 0; k <= N; k++)
{
factor *= (2 * k + 1);
summand2[k] = ((1 / sqrtl(M_PI * 2) ) * powl(z[p], 2 * k + 1) / factor);
//printf("%Le \n", factor);
sum2[p] += summand2[k];
}
sum2[p] = sum2[p] * expl((-powl(z[p],2)) / 2);
}
for (p = 0; p < 4; p++)
{
printf("The sum obtained for z between %d - %d \
\nusing the first formula is %Lf \n", z[p], z[p+1], sum1[p+1]);
printf("The sum obtained for z between %d - %d \
\nusing the second formula is %Lf \n", z[p], z[p+1], sum2[p+1]);
}
return 0;
}
The working code without the outermost for loop is
#include <stdio.h>
#include <math.h>
#include <limits.h>
#include <stdlib.h>
#define N 1200
long double
factorial(long double n)
{
//Here s is the free parameter which is increased by one in each step and
//pro is the initial product and by setting pro to be 0 we also cover the
//case of zero factorial.
int s = 1;
long double pro = 1;
//Here pro stands for product.
if (n < 0)
printf("Factorial is not defined for a negative number \n");
else {
while (n >= s) {
pro *= s;
s++;
}
return pro;
}
}
int main()
{
// Since the function given is the standard normal distribution
// probability density function we have mean = 0 and variance = 1.
// Hence we also have z = x; while dealing with only positive values of
// x and keeping in mind that the PDF is symmetric around the mean.
long double * summand1 = malloc(N * sizeof(long double));
long double * summand2 = malloc(N * sizeof(long double));
int k, z = 3;
long double sum1 = 0, sum2 = 0, pro = 1.0;
for (k = 0; k <= N; k++)
{
summand1[k] = (1 / sqrtl(M_PI * 2) )* powl(-1, k) * powl(z, 2 * k + 1) / ( factorial(k) * (2 * k + 1) * powl(2, k));
sum1 += summand1[k];
}
//Wolfamalpha site gives the same value here
printf("The sum obtained for z between 0-3 using the first formula is %Lf \n", sum1);
for (k = 0; k <= N; k++)
{
pro *= (2 * k + 1);
summand2[k] = ((1 / sqrtl(M_PI * 2) * powl(z, 2 * k + 1) / pro));
//printf("%Le \n", pro);
sum2 += summand2[k];
}
sum2 = sum2 * expl((-powl(z,2)) / 2);
printf("The sum obtained for z between 0-3 using the second formula is %Lf \n", sum2);
return 0;
}
I'm quite certain that the problem is in factor not being set back to 1 in the outer loop..
factor *= (2 * k + 1); (in the loop that calculates sum2.)
In the second version provided the one that works it starts with z=3
However in the first loop since you do not clear it between iterations on p by the time you reach z[2] it already is a huge number.
EDIT: Possible help with precision..
Basically you have a huge number powl(z[p], 2 * k + 1) divided by another huge number factor. huge floating point numbers lose their precision. The way to avoid that is to perform the division as soon as possible..
Instead of first calculating powl(z[p], 2 * k + 1) and dividing by factor :
- (z[p]z[p] ... . * z[p]) / (1*3*5*...(2*k+1))`
rearrange the calculation: (z[p]/1) * (z[p]^2/3) * (z[p]^2/5) ... (z[p]^2/(2*k+1))
You can do this in sumand2 calculation and a similar trick in summand1