#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import array

UNDETERMINED = -1


# based on http://dai1741.github.io/maximum-algo-2012/docs/dynamic-programming/
class Knapsack(object):

    def __init__(self, number, weights):
        # Number of the weights.
        self.number = number
        # Weights of the weights.
        self.weights = weights
        # Array for memoization.
        self.memo = [array.array("i", [UNDETERMINED] * (sum(weights) + 1))
                     for x in range(number + 1)]

    def rec_memo(self, i, j):
        m = self.memo[i][j]
        if m != UNDETERMINED:
            return m
        elif i >= self.number:  # if no weight is left
            result = 0
        elif j < self.weights[i]:  # if the weight i cannot be on the pan
            result = self.rec_memo(i + 1, j)
        else:  # the value of the weight: the weight of the weight
            result = max(self.rec_memo(i + 1, j),
                         self.rec_memo(i + 1, j - self.weights[i]) + self.weights[i])
        self.memo[i][j] = result
        return result

    def solve(self, weight_limit, start_number=0):
        return self.rec_memo(start_number, weight_limit)


def main():
    n = int(input())
    ws = array.array("i", (int(w) for w in input().split()))
    (d, m) = divmod(sum(ws), 2)
    if m == 1:
        print("impossible")
    else:
        k = Knapsack(n, ws)
        if k.solve(d) == d:
            print("possible")
        else:
            print("impossible")


if __name__ == "__main__":
    main()