Consider that for a supervised learning task, the objective function being minimized is $f_w(x)=wx$, where $x\in \mathbb{R}$ is the input and $w\in \mathbb{R}$ is the parameter. Stochastic Gradient Descent with learning rate of 0.10 is used for parameter updates.

Suppose that at the end of iteration $i$, the value of $w$ becomes 10.00.

Let $x=10.00$ be the input for iteration $(i+1)$.

The value of $w$ at the end of iteration $(i+1)$ is __________ . (Rounded off to two decimal places)

Consider designing a linear classifier

$$y=sign(f(x;w,b)),f(x;w,b)=w^{T}x+b$$
on a dataset $D=\{(x_1,y_1),(x_2,y_2),...,(x_N,y_N)\}$, $x_i\in {\mathbb{R}}^d$, $y_i\in \{+1,-1\}$, $i=1,2,...,N$. Recall that the sign function outputs $+1$ if the argument is positive, and $-1$ if the argument is non-positive. The parameters $w$ and $b$ are updated as per the following training algorithm:

$$w_{new}=w_{old}+y_n{x}_n,b_{new}=b_{old}+y_n$$
whenever $sign(f(x_n;w_{old},b_{old}))\ne y_n$. In other words, whenever the classifier wrongly predicts a sample $(x_n,y_n)$ from the dataset, $w_{old}$ gets updated to $w_{new}$, and likewise $b_{old}$ gets updated to $b_{new}$. Consider the case $(x_n,+1)$, $f(x_n;w_{old},b_{old})<0$. Then