def __call__(self, data: InputData) -> ResultData:
(x, y) = data.initial
path = [(x, y)]
grad = (data.df_dx1(x, y), data.df_dx2(x, y))
calls_count = 2
f_calls = 0
while (grad[0]**2 + grad[1]**2) >= data.eps**2:
(t_x, t_y) = (x - self.alpha * grad[0], y - self.alpha * grad[1])
f_calls += 2
if data.function(t_x, t_y) > data.function(
x,
y) - self.delta * self.alpha * (grad[0]**2 + grad[1]**2):
self.alpha *= self.delta
continue
(x, y) = (t_x, t_y)
grad = (data.df_dx1(x, y), data.df_dx2(x, y))
calls_count += 2
path.append((x, y))
return ResultData(self.name, (x, y), data.function(x, y), f_calls,
calls_count, path)
def __call__(self, data: InputData) -> ResultData:
(x, y) = data.initial
path = [(x, y)]
grad = (data.df_dx1(x, y), data.df_dx2(x, y))
calls_count = 2
while (grad[0] * grad[0] + grad[1] * grad[1]) >= data.eps * data.eps:
(x, y) = (x - self.alpha * grad[0], y - self.alpha * grad[1])
grad = (data.df_dx1(x, y), data.df_dx2(x, y))
calls_count += 2
path.append((x, y))
return ResultData(self.name, (x, y), data.function(x, y), 0,
calls_count, path)
def __call__(self, data: InputData) -> ResultData:
(x, y) = data.initial
path = [(x, y)]
dx = data.df_dx1(x, y)
x1 = x - self.alpha * dx
dy = data.df_dx2(x1, y)
y1 = y - self.alpha * dy
calls_count = 2
f_calls = 0
while ((x1 - x)**2 + (y1 - y)**2) >= data.eps**2:
(x, y) = (x1, y1)
dx = data.df_dx1(x, y)
x1 = x - self.alpha * dx
dy = data.df_dx2(x1, y)
y1 = y - self.alpha * dy
calls_count += 2
path.append((x, y))
path.append((x1, y1))
return ResultData(self.name, (x1, y1), data.function(x, y), f_calls,
calls_count, path)