// author: Alexander Rass import java.util.*; public class Alex_O_N_Scanner{ public static void main(String args[]){ new Alex_O_N_Scanner().main(); } int x[], y[]; int gcd(int a, int b){ if(b == 0)return a; return gcd(b, a % b); } // memoization of sampled gradients int S = 0; // number of already sampled gradients HashMap gradients; class Pair{ int a,b; Pair(int aa, int bb){a = aa; b = bb;} public int hashCode(){ return a * 10007 + b; } public boolean equals(Object o){ Pair op = (Pair) o; return a == op.a && b == op.b; } } void insertGradient(int id1, int id2){ // make gradient unique int dx = x[id1] - x[id2]; int dy = y[id1] - y[id2]; int g = gcd(Math.abs(dx), Math.abs(dy)); dx /= g; dy /= g; if(dx < 0 || (dx == 0 && dy < 0)){ dx *= -1; dy *= -1; } // memoization of gradient Pair p = new Pair(dx,dy); if(gradients.containsKey(p))gradients.put(p, gradients.get(p) + 1); else gradients.put(p, 1); S++; } int main() { Scanner scan = new Scanner(System.in); gradients = new HashMap(); int N = scan.nextInt(); int p = scan.nextInt(); if(2 * 100 >= N * p){ System.out.print("possible\n"); return 0; } x = new int [N]; y = new int [N]; for(int i = 0; i < N; i++){ x[i] = scan.nextInt(); y[i] = scan.nextInt(); } if(N * (long)N < 10000){ // sample all gradients if N is small for(int i = 0; i < N; i++){ for(int j = 0; j < i; j++){ insertGradient(i,j); } } } else { // sample 10^4 gradients if N is large Random rand = new Random(1234567899876543L); for(int i = 0; i < 10000; i++){ int a = rand.nextInt(N); int b = rand.nextInt(N); while(b == a)b = rand.nextInt(N); insertGradient(a,b); } } ArrayList numgra = new ArrayList(); for(Pair it: gradients.keySet())numgra.add(gradients.get(it)); int numgra2[] = new int[numgra.size()]; for(int i = 0; i < numgra.size(); i++)numgra2[i] = numgra.get(i); Arrays.sort(numgra2); int limit = numgra2[Math.max(numgra2.length - 20, 0)]; int DONE = 0; for(Pair it: gradients.keySet()){ int value = gradients.get(it); if(value < limit)continue; if(value == limit && DONE >= 20)continue; DONE++; { HashMap t = new HashMap(); // generate orthogonal vector to gradient long dx = -(it.b); long dy = it.a; // scalar product with orthogonal vector results in value which identifies // the position of the line for(int i = 0; i < N; i++){ long scal = dx * x[i] + dy * y[i]; if(t.containsKey(scal))t.put(scal, t.get(scal)+1); else t.put(scal, 1); } for(Long l: t.keySet()){ if(t.get(l) * 100 >= N * p){ System.out.print("possible\n"); return 0; } } } } System.out.print("impossible\n"); return 0; } }