import numpy as np
class g:

    def test(self,x):
        e = 2.71828182845904590
        return x[0]**3+e**x[0]+x[1]**4+x[0]+x[1]-2


    def gradient_descent_step1(self,x):
        self.alpha=0.01
        return [x[0]+self.alpha,x[1]],[x[0]-self.alpha,x[1]],[x[0],x[1]+self.alpha],[x[0],x[1]-self.alpha]

    def outpower(self,x1,x2,x3,x4):
 
        self.y1 = self.test(x1)
        self.y2 = self.test(x2)
        self.y3 = self.test(x3)
        self.y4 = self.test(x4)
        return self.y1,self.y2,self.y3,self.y4

    def gradient_descent_step2(self,x,y1,y2,y3,y4):

        gradient_x0 = (y1-y2)/(2*self.alpha)
        gradient_x1 = (y3-y4)/(2*self.alpha)
 

        self.step= 0.01
        x[0] = x[0]-np.sign(gradient_x0)*self.step
        x[1] = x[1]-np.sign(gradient_x1)*self.step

        return [x[0],x[1]]

x=[0.0,0.0]
g=g()

for i  in range(40):
    x1,x2,x3,x4=g.gradient_descent_step1(x)
    y1, y2, y3, y4=g.outpower(x1,x2,x3,x4)
    x = g.gradient_descent_step2(x,y1,y2,y3,y4)
 
    print ('x={:6f},{:6f},problem(x)={:6f}'.format(x[0],x[1],g.test(x)))