Parameter after one SGD step on a linear objective

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)