import random	
import math

def annealingoptimize(T=12000, cool=0.99994):
    dimension=2
    step=(C+D-11)*1.5/1989+0.01
    vec=[C*0.7,D*0.7]
    newvec = vec[:]
    while T > 0.0001:
        i = random.randint(0, dimension-1)
        dir = random.random()
        dir = (dir - 0.5) * step

        if i==0:
            stock_c=C-0.75*(vec[0]+dir)-2/7*vec[1]
            stock_d=D-0.25*(vec[0]+dir)-5/7*vec[1]
        else:
            stock_c=C-0.75*vec[0]-2/7*(vec[1]+dir)
            stock_d=D-0.25*vec[0]-5/7*(vec[1]+dir)

        if stock_c<0 or stock_d<0:
            newvec[i] = vec[i]
        else:
            newvec[i] = vec[i] + dir

        newcost = costf(newvec)
        cost = costf(vec)

        p = pow(math.e, -abs(newcost - cost) / T)

        if(newcost > cost or random.random() < p):
            vec[i] = newvec[i]
        T = T * cool
    return vec
	
def costf(vec):
    return (1000*vec[0]+2000*vec[1])

C,D=[int(i) for i in input().split()]
ans=0
k_min=[]
for i in range(6):
    kkk=annealingoptimize()
    cost=costf(kkk)
    ans=max(ans,cost)
#    print (kkk, cost)
print(ans)