Ashish has an array a of size n.

A subsequence of a is defined as a sequence that can be obtained from a by deleting some elements (possibly none), without changing the order of the remaining elements.

Consider a subsequence s of a. He defines the cost of s as the minimum between: 

  * The maximum among all elements at odd indices of s. 
  * The maximum among all elements at even indices of s. 



Note that the index of an element is its index in s, rather than its index in a. The positions are numbered from 1. So, the cost of s is equal to min(max(s_1, s_3, s_5, …), max(s_2, s_4, s_6, …)).

For example, the cost of \{7, 5, 6\} is min( max(7, 6), max(5) ) = min(7, 5) = 5.

Help him find the minimum cost of a subsequence of size k.

Input

The first line contains two integers n and k (2 ≤ k ≤ n ≤ 2 ⋅ 10^5) — the size of the array a and the size of the subsequence.

The next line contains n integers a_1, a_2, …, a_n (1 ≤ a_i ≤ 10^9) — the elements of the array a.

Output

Output a single integer — the minimum cost of a subsequence of size k.

Examples

Input


4 2
1 2 3 4


Output


1

Input


4 3
1 2 3 4


Output


2

Input


5 3
5 3 4 2 6


Output


2

Input


6 4
5 3 50 2 4 5


Output


3

Note

In the first test, consider the subsequence s = \{1, 3\}. Here the cost is equal to min(max(1), max(3)) = 1.

In the second test, consider the subsequence s = \{1, 2, 4\}. Here the cost is equal to min(max(1, 4), max(2)) = 2.

In the fourth test, consider the subsequence s = \{3, 50, 2, 4\}. Here the cost is equal to min(max(3, 2), max(50, 4)) = 3.
## necessary imports
import sys
input = sys.stdin.readline
# from math import ceil, floor, factorial;

def ceil(x, y):
    return (x + y - 1)//y;

# swap_array function
def swaparr(arr, a,b):
    temp = arr[a];
    arr[a] = arr[b];
    arr[b] = temp

## gcd function
def gcd(a,b):
    if b == 0:
        return a
    return gcd(b, a % b);

## nCr function efficient using Binomial Cofficient
def nCr(n, k): 
    if(k > n - k): 
        k = n - k 
    res = 1
    for i in range(k): 
        res = res * (n - i) 
        res = res / (i + 1) 
    return int(res) 

## upper bound function code -- such that e in a[:i] e < x;
def upper_bound(a, x, lo=0):
    hi = len(a)
    while lo < hi:
        mid = (lo+hi)//2
        if a[mid] < x:
            lo = mid+1
        else:
            hi = mid
    return lo

## prime factorization
def primefs(n):
    ## if n == 1    ## calculating primes
    primes = {}
    while(n%2 == 0 and n > 0):
        primes[2] = primes.get(2, 0) + 1
        n = n//2
    for i in range(3, int(n**0.5)+2, 2):
        while(n%i == 0 and n > 0):
            primes[i] = primes.get(i, 0) + 1
            n = n//i
    if n > 2:
        primes[n] = primes.get(n, 0) + 1
    ## prime factoriazation of n is stored in dictionary
    ## primes and can be accesed. O(sqrt n)
    return primes

## MODULAR EXPONENTIATION FUNCTION
def power(x, y, p): 
    res = 1
    x = x % p  
    if (x == 0) : 
        return 0
    while (y > 0) : 
        if ((y & 1) == 1) : 
            res = (res * x) % p 
        y = y >> 1      
        x = (x * x) % p 
    return res 

## DISJOINT SET UNINON FUNCTIONS
def swap(a,b):
    temp = a
    a = b
    b = temp
    return a,b

# find function with path compression included (recursive)
# def find(x, link):
#     if link[x] == x:
#         return x
#     link[x] = find(link[x], link);
#     return link[x];

# find function with path compression (ITERATIVE)
def find(x, link):
    p = x;
    while( p != link[p]):
        p = link[p];
    
    while( x != p):
        nex = link[x];
        link[x] = p;
        x = nex;
    return p;


# the union function which makes union(x,y)
# of two nodes x and y
def union(x, y, link, size):
    x = find(x, link)
    y = find(y, link)
    if size[x] < size[y]:
        x,y = swap(x,y)
    if x != y:
        size[x] += size[y]
        link[y] = x

## returns an array of boolean if primes or not USING SIEVE OF ERATOSTHANES
def sieve(n): 
    prime = [True for i in range(n+1)] 
    p = 2
    while (p * p <= n): 
        if (prime[p] == True): 
            for i in range(p * p, n+1, p):
                prime[i] = False
        p += 1
    return prime

#### PRIME FACTORIZATION IN O(log n) using Sieve ####
MAXN = int(1e7 + 5)
def spf_sieve():
    spf[1] = 1;
    for i in range(2, MAXN):
        spf[i] = i;
    for i in range(4, MAXN, 2):
        spf[i] = 2;
    for i in range(3, ceil(MAXN ** 0.5), 2):
        if spf[i] == i:
            for j in range(i*i, MAXN, i):
                if spf[j] == j:
                    spf[j] = i;
    ## function for storing smallest prime factors (spf) in the array

################## un-comment below 2 lines when using factorization #################
# spf = [0 for i in range(MAXN)]
# spf_sieve() 
def factoriazation(x):
    ret = {};
    while x != 1:
        ret[spf[x]] = ret.get(spf[x], 0) + 1;
        x = x//spf[x]
    return ret
    ## this function is useful for multiple queries only, o/w use
    ## primefs function above. complexity O(log n)

## taking integer array input
def int_array():
    return list(map(int, input().strip().split()))
## taking string array input
def str_array():
    return input().strip().split();

#defining a couple constants
MOD = int(1e9)+7;
CMOD = 998244353;
INF = float('inf'); NINF = -float('inf');

################### ---------------- TEMPLATE ENDS HERE ---------------- ###################

def check(x, cur):
    ans = 0;
    for i in range(n):
        if not cur:
            ans += 1;
            cur ^= 1;
        else:
            if a[i] <= x:
                ans += 1;
                cur ^= 1;
    return ans >= k;

def bin_search(lo, hi):
    while(lo < hi):
        mid = (lo + hi)//2;
        if(check(mid, 0) or check(mid, 1)):
            hi = mid;
        else:
            lo = mid + 1;
    return lo;
        
n, k = int_array(); a = int_array();
ans = bin_search(0, int(1e9 + 1));
print(ans);