$ {L}_{1} $ Norm Regularized Least Squares - Strict Convexity Property

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My objective function that is to be minimized is as follows:

$$\|y-Ax\|_2^2 + \alpha\|Lx\|_1$$

where $L$ is the gradient operator.

Now this problem seems convex because the first term is quadratic and hence convex and the second is a norm so it has to be convex (the triangular inequality), but is it also strictly convex? and why?