# Why L1 regularization can “zero out the weights” and therefore leads to sparse models? [duplicate]

I'm aware there is a very relevant explanation on L1 regularization's effect on feature selection at here: Why L1 norm for sparse models [Ref. 1].

To better understand it I'm reading Google's tutorial on Regularization for Sparsity: L₁ Regularization [Ref. 2]. When it comes to the following part, there's some statements I emphasized that I do not understand:

You can think of the derivative of L1 as a force that subtracts some constant from the weight every time. However, thanks to absolute values, L1 has a discontinuity at 0, which causes subtraction results that cross 0 to become zeroed out. For example, if subtraction would have forced a weight from +0.1 to -0.2, L1 will set the weight to exactly 0. Eureka, L1 zeroed out the weight.

I imagine when it says "L1 has a discontinuity at 0" it means the loss of the L1 like in the following figure [Ref. 1]: But why it will "cause subtraction results that cross 0 to become zeroed out"? Why "if subtraction would have forced a weight from +0.1 to -0.2, L1 will set the weight to exactly 0"?

Is it related to that L1 is not differentiable at $$w = 0$$?

So, consider that every time step, the position $$x$$ (weight or whatever you're regularizing) experiences a force that applies a total acceleration over that time step of $$-k\, \textrm{sgn}(x)$$.
Suppose $$x$$ is smaller than $$k$$. It seems like applying the force would make $$x$$ overshoot zero. However, if you subdivide the time step into smaller time steps and $$k$$ into smaller total accelerations (since the force is integrated over smaller periods), in the limit of subdivision $$x$$ simply goes to zero.