231 views

1 Answer

1 1 vote
Using the definition of GD and the requirements in the problem statement we are looking for $\eta$ such that:

$$
x_*=x_t-\eta \nabla f\left(x_t\right) \Longleftrightarrow 0=2-\eta \nabla f(2) \Longleftrightarrow \eta \cdot(2 \cdot 2)=2 \Longleftrightarrow \eta=\frac{1}{2}
$$
 
Answer:
Position:
Show:

Related questions

0 0 votes
1 1 answer
176
176 views
GO Classes asked Mar 15, 2025
176 views
When the algorithms converge, stochastic gradient descent always finds the same solution as gradient descent.(Please enter 1 for True and 0 for False)
0 0 votes
1 1 answer
161
161 views
GO Classes asked Mar 15, 2025
161 views
Suppose we run gradient descent with a fixed learning rate of $\alpha=0.1$ to minimize the 2D function $f(x, y)=5+x^2+y^2+5 x y$.The gradient of this function is$$\nabla_...
0 0 votes
0 0 answers
220
220 views
GO Classes asked Mar 15, 2025
220 views
Which of the following is not a true statement about gradient descent (GD) vs. stochastic gradient descent (SGD)?Both provide unbiased estimates of the true gradient at e...
0 0 votes
0 0 answers
222
222 views
GO Classes asked Mar 15, 2025
222 views
Suppose we are performing gradient descent to minimize the empirical risk of a linear regression model $y=\beta_0+\beta_1 x_1+\beta_2 x_1^2+\beta_3 x_2$ on a dataset with...