# Derivation of Soft Thresholding Operator / Proximal Operator of ${L}_{1}$ Norm

I was going through the derivation of soft threholding at http://dl.dropboxusercontent.com/u/22893361/papers/Soft%20Threshold%20Proof.pdf.

It says the three unique solutions for

$\operatorname{arg min} \|x-b\|_2^2 + \lambda\|x\|_1$ is given by

$\operatorname{arg min} \|x-b\|^2 + \lambda x$ assuming $x > 0$

$\operatorname{arg min} \|x-b\|^2 - \lambda x$ assuming $x < 0$

$\operatorname{arg min} \|x-b\|^2 = 0$ assuming $x = 0$

I am confused in the third case. For $x = 0$, how come the solution is zero. I mean when x = 0, I will have $\operatorname{arg min} \|x-b\|^2$. If I solve this, I will get x = b, the solution then how come it is $x = 0$

• That's poorly written. What they mean is that you minimize the objective over the restricted domains $x\in(0,\infty)$, $x\in(-\infty,0)$ and $x\in\{0\}$ respectively. In the third case, at what point in $\{0\}$ is the minimum attained? Well, there is only one point in $\{0\}$; it is $x=0$. Still, this is not a very good way of going about it, and you may want to look for a better reference.
– user856
Aug 19, 2013 at 16:42

I wrote a more detailed derivation of the soft-thresholding operator, following the source you mention and other ones. I hope it helps.

The soft-thresholding is just the proximal mapping of the $$l_1$$-norm. Let $$f(x) = \lambda\|x\|_1$$, then the proximal mapping of $$f$$ is defined as

$$$$\operatorname{prox}_f(x) = \operatorname{argmin}_z\{\frac{1}{2}\|x-z\|^2_2 + \lambda\|z\|_1 \}$$$$

The optimality condition for the previous problem is

$$$$0 \in \nabla(\frac{1}{2}\|x-z\|^2_2) + \partial(\lambda\|z\|_1) \Leftrightarrow 0 \in z-x + \lambda\partial\|z\|_1$$$$

The $$l_1$$-norm is separable and thus we can consider each of its components separately. Let's examine first the case where $$z_i \neq 0$$. Then, $$\partial \|z_i\|=\operatorname{sign}(z_i)$$ and the optimum $$z_i^*$$ is obtained as

$$$$0 = z_i-x_i + \lambda \operatorname{sign}(z_i) \Leftrightarrow z_i^* = x_i - \lambda \operatorname{sign}(z_i^*)$$$$

Note also that if $$z_i^* < 0$$, then $$x_i < -\lambda$$ and equivalently if $$z_i^* > 0 \Rightarrow x_i > \lambda$$. Thus, $$|x_i| > \lambda$$ and $$\operatorname{sign}(z_i^*) = \operatorname{sign}(x_i)$$. Substituting in the previous equation we get

$$$$z_i^* = x_i - \lambda \operatorname{sign}(x_i)$$$$

In the case where $$z_i = 0$$, the subdifferential of the $$l_1$$-norm is the interval $$[-1,1]$$ and the optimality condition is

$$$$0 \in -x_i + \lambda[-1,1] \Leftrightarrow x_i \in [-\lambda,\lambda] \Leftrightarrow |x_i| \leq \lambda$$$$

Putting all together we get

$$$$[\operatorname{prox}_f(x)]_i = z_i^* = \left\{ \begin{array}{lr} 0 & \text{if } |x_i| \leq \lambda \\ x_i - \lambda \operatorname{sign}(x_i) & \text{if } |x_i| > \lambda \end{array}\right.$$$$

The previous equation can also be written as

\begin{align*} [\operatorname{prox}_f(x)]_i &= \operatorname{sign}(x_i)\max(|x_i|-\lambda, 0) \\ &= \operatorname{sign}(x_i)(|x_i|-\lambda)_+ \end{align*}

where $$(\cdot)_+$$ denotes the positive part.

• I think your derivation is totally wrong. You have mixed up x and z Oct 5, 2013 at 18:39
• You are right there is indeed a mistake but it is only in the definition of proximal mapping, where I wrote x when it should be z. I think it is correct now
– skd
Oct 5, 2013 at 20:35
• Hey. if the the objective is argmin $1/2ax^2 bx + \lambda|c+x|_1$, the result is $x = -c + sign(c-b/a)max(|c-b/a|, \lambda/a),0)$. I didn't get how it was derived? Oct 17, 2013 at 1:06
• Is that another question? What are a and b, scalars? In that case that problem is cubic in x?? Maybe you are missing something?
– skd
Oct 18, 2013 at 12:06
• @skd you forget 1/2 in front of $\|x-z \|^2_2$.
– E.J.
Oct 15, 2014 at 2:20

Suppose $\hat{x} \in \mathbb R$ and we want to find a minimizer of $f(x) = |x| + \frac{1}{2t}(x - \hat{x})^2$. We want to make $|x|$ small without straying too far from $\hat{x}$.

Consider the case where $\hat{x} > 0$.

In this case there's no reason to let $x$ be negative. Because if $x$ is negative, then $-x$ is closer to $\hat{x}$ and $-x$ has the same absolute value as $x$.

So we need to minimize the quadratic function $g(x) = x + \frac{1}{2t} (x - \hat{x})^2$ over $[0,\infty)$. The graph of $g$ is a parabola that opens upwards, and we can minimize $g$ using techniques from pre-calculus.

The case where $\hat{x} < 0$ is similar.

• Sorry to respond to such an old answer but I had a small question. Following your steps (with $t=1$), I came to the conclusion that $g$ is minimized when $x = \hat{x}-1$. Knowing what the soft threshhold operator looks like, this makes sense when $\hat{x} \geq 1$ but we only assumed that $\hat{x} >0$? Jan 30, 2018 at 10:41

One could write the Soft Thresholding as:

$$x - \lambda \operatorname{sgn} \left( x \right) \min \left( \left| \frac{x}{\lambda} \right|, 1 \right)$$

This is of the form $$x - \lambda g \left( \frac{x}{\lambda} \right)$$.
Using the trick done in my answer to Closed Form Solution of $$\arg \min_{x} {\left\| x − y \right\|}_{2}^{2} + \lambda {\left\| x \right\|}_{2}$$ - Tikhonov Regularized Least Squares - it might suggest that $$\operatorname{sgn} \left( x \right) \min \left( \left| x \right|, 1 \right)$$ is the projection to $${L}_{1}$$ Unit Ball (Which doesn't have closed form solution).

So what's wrong here?

Remark
As noted below - The formula states the projection to the dual ball.

• The quantity you're contemplating is the projection onto the dual ball, namely the $\ell_\infty$ ball. This is because the convex conjugate of a norm is the indicator function of the dual ball. Now use Moreau's decomposition. Also, as you rightly noted (with some hesitation), projecting onto the L1 ball has no closed form solution... Mar 13, 2018 at 21:05