Covering segments by points - algorithm

I did search and looked at these below links but it didn't help .
Point covering problem
Segments poked (covered) with points - any tricky test cases?
Need effective greedy for covering a line segment
Problem Description:
You are given a set of segments on a line and your goal is to mark as
few points on a line as possible so that each segment contains at least
one marked point
Task.
Given a set of n segments {[a0,b0],[a1,b1]....[an-1,bn-1]} with integer
coordinates on a line, find the minimum number 'm' of points such that
each segment contains at least one point .That is, find a set of
integers X of the minimum size such that for any segment [ai,bi] there
is a point x belongs X such that ai <= x <= bi
Output Description:
Output the minimum number m of points on the first line and the integer
coordinates of m points (separated by spaces) on the second line
Sample Input - I
3
1 3
2 5
3 6
Output - I
1
3
Sample Input - II
4
4 7
1 3
2 5
5 6
Output - II
2
3 6
I didn't understand the question itself. I need the explanation, on how to solve this above problem, but i don't want the code. Examples would be greatly helpful

Maybe this formulation of the problem will be easier to understand. You have n people who can each tolerate a different range of temperatures [ai, bi]. You want to find the minimum number of rooms to make them all happy, i.e. you can set each room to a certain temperature so that each person can find a room within his/her temperature range.
As for how to solve the problem, you said you didn't want code, so I'll just roughly describe an approach. Think about the coldest room you have. If making it one degree warmer won't cause anyone to no longer be able to tolerate that room, you might as well make the increase, since that can only allow more people to use that room. So the first temperature you should set is the warmest one that the most cold-loving person can still tolerate. In other words, it should be the smallest of the bi. Now this room will satisfy some subset of your people, so you can remove them from consideration. Then repeat the process on the remaining people.
Now, to implement this efficiently, you might not want to literally do what I said above. I suggest sorting the people according to bi first, and for the ith person, try to use an existing room to satisfy them. If you can't, try to create a new one with the highest temperature possible to satisfy them, which is bi.

Yes the description is pretty vague and the only meaning that makes sense to me is this:
You got some line
Segment on a line is defined by l,r
Where one parameter is distance from start of line and second is the segments length. Which one is which is hard to tell as the letters are not very usual for such description. My bet is:
l length of segment
r distance of (start?) of segment from start of line
You want to find min set of points
So that each segment has at least one point in it. That mean for 2 overlapped segments you need just one point ...
Surely there are more option how to solve this, the obvious is genere & test with some heuristics like genere combinations only for segments that are overlapped more then once. So I would attack this task in this manner (using assumed terminology from #2):
sort segments by r
add number of overlaps to your segment set data
so the segment will be { r,l,n } and set the n=0 for all segments for now.
scan segments for overlaps
something like
for (i=0;i<segments;i++) // loop all segments
for (j=i+1;j<segments;j++) // loop all latter segments until they are still overlapped
if ( segment[i] and segment [j] are overlapped )
{
segment[i].n++; // update overlap counters
segment[j].n++;
}
else break;
Now if the r-sorted segments are overlapped then
segment[i].r <=segment[j].r
segment[i].r+segment[i].l>=segment[j].r
scan segments handling non overlapped segments
for each segment such that segment[i].n==0 add to the solution point list its point (middle) defined by distance from start of line.
points.add(segment[i].r+0.5*segment[i].l);
And after that remove segment from the list (or tag it as used or what ever you do for speed boost...).
scan segments that are overlapped just once
So if segment[i].n==1 then you need to determine if it is overlapped with i-1 or i+1. So add the mid point of the overlap to the solution points and remove i segment from list. Then decrement the n of the overlapped segment (i+1 or i-1)` and if zero remove it too.
points.add(0.5*( segment[j].r + min(segment[i].r+segment[i].l , segment[j].r+segment[j].l )));
Loop this whole scanning until there is no new point added to the solution.
now you got only multiple overlaps left
From this point I will be a bit vague for 2 reasons:
I do not have this tested and I d not have any test data to validate not to mention I am lazy.
This smells like assignment so there is some work/fun left for you.
From start I would scann all segments and remove all of them which got any point from the solution inside. This step you should perform after any changes in the solution.
Now you can experiment with generating combination of points for each overlapped group of segments and remember the minimal number of points covering all segments in group. (simply by brute force).
There are more heuristics possible like handling all twice overlapped segments (in similar manner as the single overlaps) but in the end you will have to do brute force on the rest of data ...
[edit1] as you added new info
The r,l means distance of left and right from the start of line. So if you want to convert between the other formulation { r',l' } and (l<=r) then
l=r`
r=r`+l`
and back
r`=l
l`=r-l`
Sorry too lazy to rewrite the whole thing ...

Here is the working solution in C, please refer to it partially and try to fix your code before reading the whole. Happy coding :) Spoiler alert
#include <stdio.h>
#include <stdlib.h>
int cmp_func(const void *ptr_a, const void *ptr_b)
{
const long *a = *(double **)ptr_a;
const long *b = *(double **)ptr_b;
if (a[1] == b[1])
return a[0] - b[0];
return a[1] - b[1];
}
int main()
{
int i, j, n, num_val;
long **arr;
scanf("%d", &n);
long values[n];
arr = malloc(n * sizeof(long *));
for (i = 0; i < n; ++i) {
*(arr + i) = malloc(2 * sizeof(long));
scanf("%ld %ld", &arr[i][0], &arr[i][1]);
}
qsort(arr, n, sizeof(long *), cmp_func);
i = j = 0;
num_val = 0;
while (i < n) {
int skip = 0;
values[num_val] = arr[i][1];
for (j = i + 1; j < n; ++j) {
int condition;
condition = arr[i][1] <= arr[j][1] ? arr[j][0] <= arr[i][1] : 0;
if (condition) {
skip++;
} else {
break;
}
}
num_val++;
i += skip + 1;
}
printf("%d\n", num_val);
for (int k = 0; k < num_val; ++k) {
printf("%ld ", values[k]);
}
free(arr);
return 0;
}

Here's the working code in C++ for anyone searching :)
#include <bits/stdc++.h>
#define ll long long
#define double long double
#define vi vector<int>
#define endl "\n"
#define ff first
#define ss second
#define pb push_back
#define all(x) (x).begin(),(x).end()
#define mp make_pair
using namespace std;
bool cmp(const pair<ll,ll> &a, const pair<ll,ll> &b)
{
return (a.second < b.second);
}
vector<ll> MinSig(vector<pair<ll,ll>>&vec)
{
vector<ll> points;
for(int x=0;x<vec.size()-1;)
{
bool found=false;
points.pb(vec[x].ss);
for(int y=x+1;y<vec.size();y++)
{
if(vec[y].ff>vec[x].ss)
{
x=y;
found=true;
break;
}
}
if(!found)
break;
}
return points;
}
int main()
{
ios_base::sync_with_stdio(false);
cin.tie(NULL);
int n;
cin>>n;
vector<pair<ll,ll>>v;
for(int x=0;x<n;x++)
{
ll temp1,temp2;
cin>>temp1>>temp2;
v.pb(mp(temp1,temp2));
}
sort(v.begin(),v.end(),cmp);
vector<ll>res=MinSig(v);
cout<<res.size()<<endl;
for(auto it:res)
cout<<it<<" ";
}

Related

Binary Lifting | Planet Queries 1 | TLE

I am solving this problem on CSES.
Given n planets, with exactly 1 teleporter on each planet which teleports us to some other planet (possibly the same), we have to solve q queries. Each query is associated with a start planet, x and a number of teleporters to traverse, k. For each query, we need to tell where we would reach after going through k teleporters.
I have attempted this problem using the binary lifting concept.
For each planet, I first saved the planets we would reach by going through 20, 21, 22,... teleporters.
Now, as per the constraints (esp. for k) provided in the question, we need only store the values till 231.
Then, for each query, starting from the start planet, I traverse through the teleporters using the data in the above created array (in 1) to mimic the binary expansion of k, the number of teleporters to traverse.
For example, if k = 5, i.e. (101)2, and the initial planet is x, I first go (001)2 = 1 planet ahead, using the array, let's say to planet y, and then (100)2 = 4 planets ahead. The planet now reached is the required result to the query.
Unfortunately, I am receiving TLE (time limit exceeded) error in the last test case (test 12).
Here's my code for reference:
#define inp(x) ll x; scanf("%lld", &x)
void solve()
{
// Inputting the values of n, number of planets and q, number of queries.
inp(n);
inp(q);
// Inputting the location of next planet the teleporter on each planet points to, with correction for 0 - based indexing
vector<int> adj(n);
for(int i = 0; i < n; i++)
{
scanf("%d", &(adj[i]));
adj[i]--;
}
// maxN stores the maximum value till which we need to locate the next reachable plane, based on constraints.
// A value of 32 means that we'll only ever need to go at max 2^31 places away from the planet in query.
int maxN = 32;
// This array consists of the next planet we can reach from any planet.
// Specifically, par[i][j] is the planet we get to, on passing through 2^j teleporters starting from planet i.
vector<vector<int>> par(n, vector<int>(maxN, -1));
for(int i = 0; i < n; i++)
{
par[i][0] = adj[i];
}
for(int i = 1; i < maxN; i++)
{
for(int j = 0; j < n; j++)
{
ll p1 = par[j][i-1];
par[j][i] = par[p1][i-1];
}
}
// This task is done for each query.
for(int i = 0; i < q; i++)
{
// x is the initial planet, corrected for 0 - based indexing.
inp(x);
x--;
// k is the number of teleporters to traverse.
inp(k);
// cur is the planet we currently are at.
int cur = x;
// For every i'th bit in k that is 1, the current planet is moved to the planet we reach to by moving through 2^i teleporters from cur.
for(int i = 0; (1 << i) <= k ; i++)
{
if(k & (1 << i))
{
cur = par[cur][i];
}
}
// Once the full binary expansion of k is used up, we are at cur, so (cur + 1) is the result because of the judge's 1 - based indexing.
cout<<(cur + 1)<<endl;
}
}
The code gives the correct output in every test case, but undergoes TLE in the final one (the result in the final one is correct too, just a TLE occurs). According to my observation the complexity of the code is O(32 * q + n), which doesn't seem to exceed the 106 bound for linear time code in 1 second.
Are there any hidden costs in the algorithm I may have missed, or some possible optimization?
Any help appreciated!
It looks to me like your code works (after fixing the scanf), but your par map could have 6.4M entries in it, and precalculating all of those might just get you over the 1s time limit.
Here are a few things to try, in order of complexity:
replace par with a single vector<int> and index it like par[i*32+j]. This will remove a lot of double indirections.
Buffer the output in a std::string and write it in one step at the end, in case there's some buffer flushing going on that you don't know about. I don't think so, but it's easy to try.
Starting at each planet, you enter a cycle in <= n steps. In O(n) time, you can precalculate the distance to the terminal cycle and the size of the terminal cycle for all planets. Using this information you can reduce each k to at most 20000, and that means you only need j <= 16.

Find the missing coordinate of rectangle

Chef has N axis-parallel rectangles in a 2D Cartesian coordinate system. These rectangles may intersect, but it is guaranteed that all their 4N vertices are pairwise distinct.
Unfortunately, Chef lost one vertex, and up until now, none of his fixes have worked (although putting an image of a point on a milk carton might not have been the greatest idea after all…). Therefore, he gave you the task of finding it! You are given the remaining 4N−1 points and you should find the missing one.
Input
The first line of the input contains a single integer T denoting the number of test cases. The description of T test cases follows.
The first line of each test case contains a single integer N.
Then, 4N−1 lines follow. Each of these lines contains two space-separated integers x and y denoting a vertex (x,y) of some rectangle.
Output
For each test case, print a single line containing two space-separated integers X and Y ― the coordinates of the missing point. It can be proved that the missing point can be determined uniquely.
Constraints
T≤100
1≤N≤2⋅105
|x|,|y|≤109
the sum of N over all test cases does not exceed 2⋅105
Example Input
1
2
1 1
1 2
4 6
2 1
9 6
9 3
4 3
Example Output
2 2
Problem link: https://www.codechef.com/problems/PTMSSNG
my approach: I have created a frequency array for x and y coordinates and then calculated the point which is coming odd no. of times.
#include <iostream>
using namespace std;
int main() {
// your code goes here
int t;
cin>>t;
while(t--)
{
long int n;
cin>>n;
long long int a[4*n-1][2];
long long int xm,ym,x,y;
for(int i=0;i<4*n-1;i++)
{
cin>>a[i][0]>>a[i][1];
if(i==0)
{
xm=abs(a[i][0]);
ym=abs(a[i][1]);
}
if(i>0)
{
if(abs(a[i][0])>xm)
{
xm=abs(a[i][0]);
}
if(abs(a[i][1])>ym)
{
ym=abs(a[i][1]);
}
}
}
long long int frqx[xm+1],frqy[ym+1];
for(long long int i=0;i<xm+1;i++)
{
frqx[i]=0;
}
for(long long int j=0;j<ym+1;j++)
{
frqy[j]=0;
}
for(long long int i=0;i<4*n-1;i++)
{
frqx[a[i][0]]+=1;
frqy[a[i][1]]+=1;
}
for(long long int i=0;i<xm+1;i++)
{
if(frqx[i]>0 && frqx[i]%2>0)
{
x=i;
break;
}
}
for(long long int j=0;j<ym+1;j++)
{
if(frqy[j]>0 && frqy[j]%2>0)
{
y=j;
break;
}
}
cout<<x<<" "<<y<<"\n";
}
return 0;
}
My code is showing TLE for inputs <10^6
First of all, your solution is not handling negative x/y correctly. long long int frqx[xm+1],frqy[ym+1] allocated barely enough memory to hold positive values, but not enough to hold negative ones.
It doesn't even matter though, as with the guarantee that abs(x) <= 109, you can just statically allocate a vector of 219 elements, and map both positive and negative coordinates in there.
Second, you are not supposed to buffer the input in a. Not only is this going to overflow the stack, is also entirely unnecessary. Write to the frequency buckets right away, don't buffer.
Same goes for most of these challenges. Don't buffer, always try to process the input directly.
About your buckets, you don't need a long long int. A bool per bucket is enough. You do not care even the least how many coordinates were sorted into the bucket, only whether the number so far was even or not. What you implemented as a separate loop can be substituted by simply toggling a flag while processing the input.
I find the answer of #Ext3h with respect to the errors adequate.
The solution, giving that you came on the odd/even quality of the problem,
can be done more straight-forward.
You need to find the x and y that appear an odd number of times.
In java
int[] missingPoint(int[][] a) {
//int n = (a.length + 1) / 4;
int[] pt = new int[2]; // In C initialize with 0.
for (int i = 0; i < a.length; ++i) {
for (int j = 0; j < 2; ++j) {
pt[j] ^= a[i][j];
}
}
return pt;
}
This uses exclusive-or ^ which is associative and reflexive 0^x=x, x^x=0. (5^7^4^7^5=4.)
For these "search the odd one" one can use this xor-ing.
In effect you do not need to keep the input in an array.

Resolve 16-Queens Problem in 1 second only

I should resolve 16-Queens Problem in 1 second.
I used backtracking algorithm like below.
This code is enough to resolve N-Queens Problem in 1 second when the N is smaller than 13.
But it takes long time if N is bigger than 13.
How can I improve it?
#include <stdio.h>
#include <stdlib.h>
int n;
int arr[100]={0,};
int solution_count = 0;
int check(int i)
{
int k=1, ret=1;
while (k < i && ret == 1) {
if (arr[i] == arr[k] ||
abs(arr[i]-arr[k]) == abs(i-k))
ret = 0;
k++;
}
return ret;
}
void backtrack(int i)
{
if(check(i)) {
if(i == n) {
solution_count++;
} else {
for(int j=1; j<=n; j++) {
arr[i+1] = j;
backtrack(i+1);
}
}
}
}
int main()
{
scanf("%d", &n);
backtrack(0);
printf("%d", solution_count);
}
Your algorithm is almost fine. A small change will probably give you enough time improvement to produce a solution much faster. In addition, there is a data structure change that should let you reduce the time even further.
First, tweak the algorithm a little: rather than waiting for the check all the way till you place all N queens, check early: every time you are about to place a new queen, check if another queen is occupying the same column or the same diagonal before making the arr[i+1] = j; assignment. This will save you a lot of CPU cycles.
Now you need to speed up checking of the next queen. In order to do that you have to change your data structure so that you could do all your checks without any loops. Here is how to do it:
You have N rows
You have N columns
You have 2N-1 ascending diagonals
You have 2N-1 descending diagonals
Since no two queens can take the same spot in any of the four "dimensions" above, you need an array of boolean values for the last three things; the rows are guaranteed to be different, because the i parameter of backtrack, which represents the row, is guaranteed to be different.
With N up to 16, 2N-1 goes up to 31, so you can use uint32_t for your bit arrays. Now you can check if a column c is taken by applying bitwise and & to the columns bit mask and 1 << c. Same goes for the diagonal bit masks.
Note: Doing a 16 Queen problem in under a second would be rather tricky. A very highly optimized program does it in 23 seconds on an 800 MHz PC. A 3.2 GHz should give you a speed-up of about 4 times, but it would be about 8 seconds to get a solution.
I would change while (k < i && ret == 1) { to while (k < i) {
and instead of ret = 0; do return 0;.
(this will save a check every iteration. It might be that your compiler does this anyway, or some other performance trick, but this might help a bit).

Algorithm to match sets with overlapping members

Looking for an efficient algorithm to match sets among a group of sets, ordered by the most overlapping members. 2 identical sets for example are the best match, while no overlapping members are the worst.
So, the algorithm takes input a list of sets and returns matching set pairs ordered by the sets with the most overlapping members.
Would be interested in ideas to do this efficiently. Brute force approach is to try all combinations and sort which obviously is not very performant when the number of sets is very large.
Edit: Use case - Assume a large number of sets already exist. When a new set arrives, the algorithm is run and the output includes matching sets (with at least one element overlap) sorted by the most matching to least (doesn't matter how many items are in the new/incoming set). Hope that clarifies my question.
If you can afford an approximation algorithm with a chance of error, then you should probably consider MinHash.
This algorithm allows estimating the similarity between 2 sets in constant time. For any constructed set, a fixed size signature is computed, and then only the signatures are compared when estimating the similarities. The similarity measure being used is Jaccard distance, which ranges from 0 (disjoint sets) to 1 (identical sets). It is defined as the intersection to union ratio of two given sets.
With this approach, any new set has to be compared against all existing ones (in linear time), and then the results can be merged into the top list (you can use a bounded search tree/heap for this purpose).
Since the number of possible different values is not very large, you get a fairly efficient hashing if you simply set the nth bit in a "large integer" when the nth number is present in your set. You can then look for overlap between sets with a simple bitwise AND followed by a "count set bits" operation. On 64 bit architecture, that means that you can look for the similarity between two numbers (out of 1000 possible values) in about 16 cycles, regardless of the number of values in each cluster. As the cluster gets more sparse, this becomes a less efficient algorithm.
Still - I implemented some of the basic functions you might need in some code that I attach here - not documented but reasonably understandable, I think. In this example I made the numbers small so I can check the result by hand - you might want to change some of the #defines to get larger ranges of values, and obviously you will want some dynamic lists etc to keep up with the growing catalog.
#include <stdio.h>
// biggest number you will come across: want this to be much bigger
#define MAXINT 25
// use the biggest type you have - not int
#define BITSPER (8*sizeof(int))
#define NWORDS (MAXINT/BITSPER + 1)
// max number in a cluster
#define CSIZE 5
typedef struct{
unsigned int num[NWORDS]; // want to use longest type but not for demo
int newmatch;
int rank;
} hmap;
// convert number to binary sequence:
void hashIt(int* t, int n, hmap* h) {
int ii;
for(ii=0;ii<n;ii++) {
int a, b;
a = t[ii]%BITSPER;
b = t[ii]/BITSPER;
h->num[b]|=1<<a;
}
}
// print binary number:
void printBinary(int n) {
unsigned int jj;
jj = 1<<31;
while(jj!=0) {
printf("%c",((n&jj)!=0)?'1':'0');
jj>>=1;
}
printf(" ");
}
// print the array of binary numbers:
void printHash(hmap* h) {
unsigned int ii, jj;
for(ii=0; ii<NWORDS; ii++) {
jj = 1<<31;
printf("0x%08x: ", h->num[ii]);
printBinary(h->num[ii]);
}
//printf("\n");
}
// find the maximum overlap for set m of n
int maxOverlap(hmap* h, int m, int n) {
int ii, jj;
int overlap, maxOverlap = -1;
for(ii = 0; ii<n; ii++) {
if(ii == m) continue; // don't compare with yourself
else {
overlap = 0;
for(jj = 0; jj< NWORDS; jj++) {
// just to see what's going on: take these print statements out
printBinary(h->num[ii]);
printBinary(h->num[m]);
int bc = countBits(h->num[ii] & h->num[m]);
printBinary(h->num[ii] & h->num[m]);
printf("%d bits overlap\n", bc);
overlap += bc;
}
if(overlap > maxOverlap) maxOverlap = overlap;
}
}
return maxOverlap;
}
int countBits (unsigned int b) {
int count;
for (count = 0; b != 0; count++) {
b &= b - 1; // this clears the LSB-most set bit
}
return count;
}
int main(void) {
int cluster[20][CSIZE];
int temp[CSIZE];
int ii,jj;
static hmap H[20]; // make them all 0 initially
for(jj=0; jj<20; jj++){
for(ii=0; ii<CSIZE; ii++) {
temp[ii] = rand()%MAXINT;
}
hashIt(temp, CSIZE, &H[jj]);
}
for(ii=0;ii<20;ii++) {
printHash(&H[ii]);
printf("max overlap: %d\n", maxOverlap(H, ii, 20));
}
}
See if this helps at all...

Travelling Salesman Problem

I am trying to develop a program in C++ from Travelling Salesman Problem Algorithm. I need a distance matrix and a cost matrix. After using all the formulas, i get a new resultant matrix. But I dont understand what that matrix shows.
Suppose the resultant matrix is:
1 2 3
4 5 6
7 8 9
Now I want to know what this matrix shows? Assume I have 3 cities to traverse.
Please tell me the flow. A sample program of this algorithm will be more favorable..
Thank you.
My Program is:
#include<iostream.h>
#include<conio.h>
#include <stdlib.h>
void main()
{
clrscr();
int a,b,c,d,ctr,j,Q=1,K=1 ;
float q0=0.7, p = 0.5 ;
int phe[3][3];
double dist[3][3] , mem[3][3],exp[3][3],eplt[3][3], rnd;
cout<<"enter the iterations, cities , ants ";
cin>>a>>b>>c;
for (int i=0;i<3;i++)
{
for (j=0;j<3;j++)
{
dist[i][j]=(double)rand()/(double)RAND_MAX;
if (i==j)
dist[i][j]=0;
}
}
for (i=0;i<3;i++)
{
for (j=0;j<3;j++)
{
cout<< dist[i][j]<<"\t";
}
cout<<"\n";
}
cout<<"pheromone matrix "<<endl;
for (i=0;i<3;i++)
{
for (j=0;j<3;j++)
{
if (i==j)
phe[i][j]=0;
else
phe[i][j]=1;
}
}
for ( i=0;i<3;i++)
{
for ( j=0;j<3;j++)
{
cout<< phe[i][j]<<"\t";
}
cout<<"\n";
}
cout<< "after iteration "<<endl;
for (i=0;i<3;i++)
{
ctr=0;
for (int k=0;k<3;k++)
{
// mem[i][k]=(rand()%b)+1;
// cout<<"memory"<<mem[i][k]<<"\n";
rnd= (double)rand()/(double)RAND_MAX;
cout<<"hhhhhhh"<<rnd;
if (rnd<=q0)
{
cout<<"Exploitation\n";
eplt[i][ctr] =(p*phe[i][k])+(Q/K);
}
else
{
cout<<"EXPLORATION\n";
eplt[i][ctr]= phe[i][k]/dist[i][k];
}
ctr++;
}
}
for (i=0;i<3;i++)
{
for (int k=0;k<3;k++)
{
cout <<eplt[i][k]<<"\t";
}
cout<<"\n";
}
getch();
}
OUTPUT:
enter the iterations, cities , ants 3
4
4
0 0.003967 0.335154
0.033265 0 0.2172
0.536973 0.195776 0
pheromone matrix
0 1 1
1 0 1
1 1 0
after iteration
hhhhhhh0.949919EXPLORATION
hhhhhhh0.356777EXPLOITATION
hhhhhhh0.356777EXPLOITATION
hhhhhhh0.356777EXPLOITATION
hhhhhhh0.356777EXPLOITATION
hhhhhhh0.356777EXPLOITATION
hhhhhhh0.949919EXPLORATION
First up, I'm guessing when you say My Program you mean The program in the paper since it is basically out of date C++. Standard library headers don't have .h appended, and conio.h is an MS-DOS header - most code that I've seen that uses that comes from Borland Turbo C++. Worth bearing in mind if you're going to try to compile that demo on a modern system.
Next up, what you're looking at is an adjacancy matrix. I don't believe that matrix is part of the output at all; I believe it is part of the model being used, for demonstration purposes. I believe, given you have a pheromone matrix, that what you're looking at here is Ant Colony Optimisation, a probabilistic method of solving the TSP and other problems that can be reduced to it.
From your output, it isn't clear where or how the result is being stored, and since this is homework, I am lazy and you're just asking for an outright answer, I'm not going to read that code. The premise of Ant Colony optimisation is that pheromone trails laid by ants, which walk the graph at random, decay over time (number of iterations). The longer it takes an ant to move along a particular vertex (distance), the more the laid pheromone decays. At this point, ants start to make decisions based on the strength of the laid pheromone along a path. So what happens is ants start to prefer certain routes over others, and continually re-inforce the pheromone along that path.
So, somewhere in there, there must be a matrix like the adjacancy matrix, storing the pheromone levels for each route. Combined with the length of the route, each iteration should detect a rate of decay.
Your input variables a, b, c are never used.
Your variable ctr is used in the exact same incremental way as the variable k of the same loop.
Your phenomone matrix indicates use of an ant colony optimization algorithm, why just not say it in your question ?
Such "iteration" should be, well, iterated, so probably the output you give us (which is not a normal output) is not the definitive solution, rather a provisory result of the algorithm.
In this post, implementation of simple solution is discussed.
Consider city 1 or 0 as the starting and ending point. Since route is
cyclic, we can consider any point as starting point.
Generate all (n-1)! permutations of cities.
Calculate cost of every permutation and keep track of minimum cost
permutation.
Return the permutation with minimum cost.
#include <bits/stdc++.h>
using namespace std;
int main(void){
int t;
scanf("%d",&t);
while(t--){
int n;
scanf("%d",&n);
int graph[n][n];
for(int i =0;i<n;i++){
for(int j =0;j<n;j++){
scanf("%d",&graph[i][j]);
}
}
vector<int> v;
int s = 0;
for(int i =0;i<n;i++){
if(i!=s){
v.push_back(i);
}
}
int ans = INT_MAX;
do{
int current_pathsum = 0;
int k = s;
for(int i = 0;i<v.size();i++){
current_pathsum += graph[k][v[i]];
k = v[i];
}
current_pathsum += graph[k][s];
ans = min(ans,current_pathsum);
}while(next_permutation(v.begin(),v.end()));
cout<<ans<<endl;
}
}

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