Least-squares with $2$-norm penalty term

I want to minimize the following cost function

$$J= \displaystyle\min_{\boldsymbol{\rm x}} \left\{\|A\boldsymbol{\rm x}_\lambda-b\|_2^2 + \lambda\|\boldsymbol{\rm x}_\lambda\|_2\right\}$$

It is a bit different from Tikhonov regularization because the penalty term is not squared. As opposed to Tikhonov, which has an analytic solution, I was not able to find such solution for $J$. Could someone give me some hint which method I can use to solve for $\boldsymbol{\rm x}_\lambda$?

• any quasi newton / trust region method for instance. you might lose some theoretical convergence speed due to the fact that $\left\|\cdot\right\|_2$ is continous but not smooth. (more explicit hint for the case of quasi-newton: CG/BFGS/limited-memory-BFGS) – Max Feb 9 '18 at 22:55
• @Max: I used CVX (cvxr.com) which uses SeDuMi to solve it but unfortunately, I have not figured out the details of the solver method. CVX gives me solutions that makes sense. However, I am trying to write my own solver. By any chance do you happen to use CVX/SeDuMi? – AFP Feb 9 '18 at 23:08
• since cvx seems to be a matlab lib, i guess you use matlab. do you have the optimization toolbox? if yes: fminunc( @(x) sum((A*x-b).^2 +$\lambda$*norm(x) , x0 ) might already do the job. de.mathworks.com/help/optim/ug/fminunc.html having said this, i would always recommend fmincon before fminunc since it has more solvers (such as L-BFGS) implemented (and you can just omit any constraints) de.mathworks.com/help/optim/ug/fmincon.html And of course it's always a good idea to implement a gradient function such that finite differences are not necessairy. – Max Feb 9 '18 at 23:20
• Thanks Max. I do have MATLAB but it does not have the optimization toolbox. – AFP Feb 9 '18 at 23:25
• if the example code on cvxr.com/cvx does work, all you need to do is to omit the constraints and to replace the objective function by sum((A*x-b).^2) +$\lambda$*norm(x). – Max Feb 10 '18 at 9:28

You can solve this problem easily using the proximal gradient method or an accelerated proximal gradient method such as FISTA. This is a powerful tool that everyone can easily learn to use. The proximal gradient method solves optimization problems of the form $$\text{minimize} \quad f(x) + g(x)$$ where $f$ and $g$ are convex and $f$ is differentiable (with a Lipschitz continuous gradient) and $g$ is "simple" in the sense that its proximal operator can be evaluated efficiently. The proximal operator of $g$ is defined as follows: $$\tag{\spadesuit} \text{prox}_{tg}(\hat x) = \arg \, \min_x \quad g(x) + \frac{1}{2t} \| x - \hat x \|_2^2.$$ The proximal operator of $g$ reduces the value of $g$ without straying too far from $\hat x$. The parameter $t > 0$ can be thought of as a "step size" that controls how far away from $\hat x$ we are allowed to move. Note that $g$ is not required to be differentiable. (But $g$ is required to be lower-semicontinuous, which is a mild condition that is usually satisfied in practice.)
The proximal gradient method iteration is $$x^{k+1} = \text{prox}_{tg}(x^k - t \nabla f(x^k)).$$ What is happening here is that, starting from the current location $x^k$, we take a gradient descent step to reduce the value of $f$, then we apply the prox-operator of $g$ to reduce the value of $g$. The step size $t > 0$ is required to satisfy $t \leq 1/L$, where $L$ is a Lipschitz constant for $\nabla f$. (There is also a line search version of the proximal gradient method that might converge faster. In the line search version the step size $t$ is chosen adaptively at each iteration.)
Your optimization problem has the form $(\spadesuit)$ where $$f(x) = \| Ax - b \|_2^2$$ and $$g(x) = \lambda \|x\|_2.$$ The gradient of $f$ can be evaluated using calculus: $$\nabla f(x) = 2 A^T(Ax - b).$$ There is a nice formula for the proximal operator of the $\ell_2$-norm: $$\text{prox}_{tg}(x) = \text{prox}_{t \lambda \| \cdot \|_2}(x) = x - P_B(x),$$ where $B$ is the $2$-norm ball of radius $t \lambda$ centered at the origin and $P_B(x)$ is the projection of $x$ onto $B$. (A formula for the proximal operator of a norm can be found for example in Vandenberghe's UCLA 236c notes. See chapter 8 "The proximal mapping", slide 8-3.)