import random
import bisect
import math

option=1     # 0:PSO 1:CFA 2:CFArank 3:hybridPSO
max_or_min=1 #0:minimize 1:maxmize
filewrite=0  # 1:writefile 2:benchmark
dimension=2

w, h=[int(i) for i in input().split()]


#--------粒　子　群　最　適　化------------------------------

"""
#評価関数
def criterion(x, y, x_min, x_max, y_min, y_max):
    if x_min<=x<=x_max and y_min<=y<=y_max:
        z=-(y+47)*math.sin(abs(x/2+(y+47))**0.5) - x*math.sin(abs(x-(y+47))**0.5)
    else:
        z=[-float("inf") if max_or_min==1 else float("inf")]
    return z
"""

#評価関数: z = 1000*x+2000*y
def criterion(x, y, x_min, x_max, y_min, y_max):
    stock_c=w-0.75*(x)-2/7*(y)
    stock_d=h-0.25*(x)-5/7*(y)
    if x_min<=x<=x_max and y_min<=y<=y_max and stock_c>=0 and stock_d>=0:
        z=1000*x+2000*y
    else:
        if max_or_min==1: z=-float("inf") 
        else: z=float("inf")
    return z


#粒子の位置の更新を行う関数
def update_position(x, y, vx, vy, x_min, x_max, y_min, y_max):
    if x_min<=x+vx<=x_max and y_min<=y+vy<=y_max :
        new_x = x + vx
        new_y = y + vy
    else:
        new_x = x
        new_y = y

    return new_x, new_y

#粒子の速度の更新を行う関数
def update_velocity(x, y, vx, vy, p, g, w, ro_max=1, c1=2.05, c2=2.05):
    #パラメーターroはランダムに与える
    ro1 = random.uniform(0, ro_max)
    ro2 = random.uniform(0, ro_max)
    phi=c1+c2
    K=2/abs(2-phi-(phi*phi-4*phi)**0.5)
    #粒子速度の更新を行う

    if option!=0:
        new_vx =K * ( w * vx + c1 * ro1 * (p[0] - x) + c2 * ro2 * (g[0] - x) )
        new_vy =K * ( w * vy + c1 * ro1 * (p[1] - y) + c2 * ro2 * (g[1] - y) )
    else:
        new_vx = w * vx + ro1 * (p[0] - x) + ro2 * (g[0] - x)
        new_vy = w * vy + ro1 * (p[1] - y) + ro2 * (g[1] - y)

    return new_vx, new_vy

def main():
    N = 1200  #粒子の数
    w = 0.5
    w_best, w_worst = 1.25, 0.25
    x_min, x_max = 0, float("inf")
    y_min, y_max = 0, float("inf")
    #粒子位置, 速度, パーソナルベスト, グローバルベストの初期化を行う
    ps = [[random.uniform(x_min, w), random.uniform(y_min, h)] for i in range(N)]
    vs = [[0.0, 0.0] for i in range(N)]

    personal_best_positions = list(ps)
    personal_best_scores = [criterion(p[0], p[1], x_min, x_max, y_min, y_max) for p in ps]
    best_particle = personal_best_scores.index(max(personal_best_scores))
    global_best_position = personal_best_positions[best_particle]

    T = 100  #世代数(ループの回数)
    for t in range(T):
        for n in range(N):
            x, y = ps[n][0], ps[n][1]
            vx, vy = vs[n][0], vs[n][1]
            p = personal_best_positions[n]

            if option>=2:
                best_list = sorted(personal_best_positions)
                mu = bisect.bisect_left( best_list, p ) + 1
                w = w_best - mu * (w_best - w_worst) / (N - 1)

            #粒子の位置の更新を行う
            new_x, new_y = update_position(x, y, vx, vy, x_min, x_max, y_min, y_max )
            ps[n] = [new_x, new_y]

            #粒子の速度の更新を行う
            new_vx, new_vy = update_velocity(new_x, new_y, vx, vy, p, global_best_position, w)
            vs[n] = [new_vx, new_vy]

            #評価値を求め, パーソナルベストの更新を行う
            score = criterion(new_x, new_y, x_min, x_max, y_min, y_max)

            if max_or_min == 1:
                if score > personal_best_scores[n]:
                    personal_best_scores[n] = score
                    personal_best_positions[n] = [new_x, new_y]
            elif max_or_min == 0:
                if score < personal_best_scores[n]:
                    personal_best_scores[n] = score
                    personal_best_positions[n] = [new_x, new_y]

        #グローバルベストの更新を行う
        if max_or_min == 1:
            if filewrite>=1: f.write(str(max(personal_best_scores))+"\n")
            best_particle = personal_best_scores.index(max(personal_best_scores))
            global_best_position = personal_best_positions[best_particle]

        elif max_or_min == 0:
            if filewrite>=1: f.write(str(min(personal_best_scores))+"\n")
            best_particle = personal_best_scores.index(min(personal_best_scores))
            global_best_position = personal_best_positions[best_particle]

    #最適解
    if max_or_min == 1:
        return max(personal_best_scores)
    elif max_or_min == 0:
        return min(personal_best_scores)

#--------------------------------------------------------------


if max_or_min == 1:
    best=-float("inf")
    for i in range(1):
        if filewrite>=2: option=i
        if filewrite>=1:
            with open('result_'+ str(option)+".txt", 'w') as f:
                best=max(best, main())
        else:
            best=max(best, main())

elif max_or_min == 0:
    best=float("inf")
    for i in range(1):
        if filewrite>=2: option=i
        if filewrite>=1:
            with open('result_'+ str(option)+".txt", 'w') as f:
                best=min(best, main())
        else:
            best=min(best, main())

print(best)

if filewrite==2:
  with open('result_0.txt', 'r') as f1, open('result_1.txt', 'r') as f2, open('result_2.txt', 'r') as f3, open('result.txt', 'w') as g:
    for i in range(100):
        a,b,c=float(f1.readline()), float(f2.readline()), float(f3.readline())
        g.write(str(a)+" "+str(b)+" "+str(c)+"\n")