# 5.1 Convex optimization-based methods  (Page 2/2)

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A newer algorithm called “l1_ls" [link] is based on an interior-point algorithm that uses a preconditioned conjugate gradient (PCG) method to approximately solve linear systems in a truncated-Newton framework. The algorithm exploits the structure of the Hessian to construct their preconditioner; thus, this is a second order method. Computational results show that about a hundred PCG steps are sufficient for obtaining accurate reconstruction. This method has been typically shown to be slower than first-order methods, but could be faster in cases where the true target signal is highly sparse.

## Fixed-point continuation

As opposed to solving the constrained formulation, an alternate approach is to solve the unconstrained formulation in [link] . A widely used method for solving ${\ell }_{1}$ -minimization problems of the form

$\underset{x}{min}\phantom{\rule{0.277778em}{0ex}}\mu {\parallel x\parallel }_{1}+H\left(x\right),$

for a convex and differentiable $H$ , is an iterative procedure based on shrinkage (also called soft thresholding; see [link] below). In the context of solving [link] with a quadratic $H$ , this method was independently proposed and analyzed in [link] , [link] , [link] , [link] , and then further studied or extended in [link] , [link] , [link] , [link] , [link] , [link] . Shrinkage is a classic method used in wavelet-based image denoising. The shrinkage operator on any scalar component can be defined as follows:

$\mathrm{shrink}\left(t,\alpha \right)=\left\{\begin{array}{cc}t-\alpha \hfill & \mathrm{if}t>\alpha ,\hfill \\ 0\hfill & \mathrm{if}-\alpha \le t\le \alpha ,\mathrm{and}\hfill \\ t+\alpha \hfill & \mathrm{if}t<-\alpha .\hfill \end{array}\right)$

This concept can be used effectively to solve [link] . In particular, the basic algorithm can be written as following the fixed-point iteration: for $i=1,...,N$ , the ${i}^{\mathrm{th}}$ coefficient of $x$ at the ${\left(k+1\right)}^{\mathrm{th}}$ time step is given by

${x}_{i}^{k+1}=\mathrm{shrink}\left({\left({x}^{k}-\tau ▽H\left({x}^{k}\right)\right)}_{i},\mu \tau \right)$

where $\tau >0$ serves as a step-length for gradient descent (which may vary with $k$ ) and $\mu$ is as specified by the user. It is easy to see that the larger $\mu$ is, the larger the allowable distance between ${x}^{k+1}$ and ${x}^{k}$ . For a quadratic penalty term $H\left(·\right)$ , the gradient $▽H$ can be easily computed as a linear function of ${x}^{k}$ ; thus each iteration of [link] essentially boils down to a small number of matrix-vector multiplications.

The simplicity of the iterative approach is quite appealing, both from a computational, as well as a code-design standpoint. Various modifications, enhancements, and generalizations to this approach have been proposed, both to improve the efficiency of the basic iteration in [link] , and to extend its applicability to various kinds of $J$ [link] , [link] , [link] . In principle, the basic iteration in [link] would not be practically effective without a continuation (or path-following) strategy [link] , [link] in which we choose a gradually decreasing sequence of values for the parameter $\mu$ to guide the intermediate iterates towards the final optimal solution.

This procedure is known as continuation ; in [link] , the performance of an algorithm known as Fixed-Point Continuation (FPC) has been compared favorably with another similar method known as Gradient Projection for Sparse Reconstruction (GPSR) [link] and “l1_ls” [link] . A key aspect to solving the unconstrained optimization problem is the choice of the parameter $\mu$ . As discussed above, for CS recovery, $\mu$ may be chosen by trial and error; for the noiseless constrained formulation, we may solve the corresponding unconstrained minimization by choosing a large value for $\mu$ .

In the case of recovery from noisy compressive measurements, a commonly used choice for the convex cost function $H\left(x\right)$ is the square of the norm of the residual . Thus we have:

$\begin{array}{cc}\hfill H\left(x\right)& ={\parallel y-\Phi x\parallel }_{2}^{2}\hfill \\ \hfill ▽H\left(x\right)& =2{\Phi }^{\top }\left(y-\Phi x\right).\hfill \end{array}$

For this particular choice of penalty function, [link] reduces to the following iteration:

${x}_{i}^{k+1}=\mathrm{shrink}\left(\left({x}^{k}-\tau ▽H{\left(y-\Phi {x}^{k}\right)}_{i},\mu \tau \right)$

which is run until convergence to a fixed point. The algorithm is detailed in pseudocode form below.

 Inputs: CS matrix $\Phi$ , signal measurements $y$ , parameter sequence ${\mu }_{n}$ Outputs: Signal estimate $\stackrel{^}{x}$ initialize: ${\stackrel{^}{x}}_{0}=0$ , $r=y$ , $k=0$ . while ħalting criterion false do 1. $k←k+1$ 2. $x←\stackrel{^}{x}-\tau {\Phi }^{T}r$ {take a gradient step} 3. $\stackrel{^}{x}←\mathrm{shrink}\left(x,{\mu }_{k}\tau \right)$ {perform soft thresholding} 4. $r←y-\Phi \stackrel{^}{x}$ {update measurement residual} end while return $\stackrel{^}{x}←\stackrel{^}{x}$ 

## Bregman iteration methods

It turns out that an efficient method to obtain the solution to the constrained optimization problem in [link] can be devised by solving a small number of the unconstrained problems in the form of [link] . These subproblems are commonly referred to as Bregman iterations . A simple version can be written as follows:

$\begin{array}{cc}\hfill {y}^{k+1}& ={y}^{k}+y-\Phi {x}^{k}\hfill \\ \hfill {x}^{k+1}& =argmin\phantom{\rule{3.33333pt}{0ex}}J\left(x\right)+\frac{\mu }{2}{\parallel \Phi x-{y}^{k+1}\parallel }^{2}.\hfill \end{array}$

The problem in the second step can be solved by the algorithms reviewed above. Bregman iterations were introduced in [link] for constrained total variation minimization problems, and was proved to converge for closed, convex functions $J\left(x\right)$ . In [link] , it is applied to [link] for $J\left(x\right)={\parallel x\parallel }_{1}$ and shown to converge in a finite number of steps for any $\mu >0$ . For moderate $\mu$ , the number of iterations needed is typically lesser than 5. Compared to the alternate approach that solves [link] through directly solving the unconstrained problem in [link] with a very large $\mu$ , Bregman iterations are often more stable and sometimes much faster.

## Discussion

All the methods discussed in this section optimize a convex function (usually the ${\ell }_{1}$ -norm) over a convex (possibly unbounded) set. This implies guaranteed convergence to the global optimum. In other words, given that the sampling matrix $\Phi$ satisfies the conditions specified in "Signal recovery via ${\ell }_{1}$ minimization" , convex optimization methods will recover the underlying signal $x$ . In addition, convex relaxation methods also guarantee stable recovery by reformulating the recovery problem as the SOCP, or the unconstrained formulation.

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