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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 1 -minimization problems of the form

min x μ x 1 + H ( x ) ,

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:

shrink ( t , α ) = t - α if t > α , 0 if - α t α , and t + α if t < - α .

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 th coefficient of x at the ( k + 1 ) th time step is given by

x i k + 1 = shrink ( ( x k - τ H ( x k ) ) i , μ τ )

where τ > 0 serves as a step-length for gradient descent (which may vary with k ) and μ is as specified by the user. It is easy to see that the larger μ is, the larger the allowable distance between x k + 1 and x k . For a quadratic penalty term H ( · ) , 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 μ 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 μ . As discussed above, for CS recovery, μ 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 μ .

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

H ( x ) = y - Φ x 2 2 H ( x ) = 2 Φ ( y - Φ x ) .

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

x i k + 1 = shrink ( ( x k - τ H ( y - Φ x k ) i , μ τ )

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

Inputs: CS matrix Φ , signal measurements y , parameter sequence μ n Outputs: Signal estimate x ^ initialize: x ^ 0 = 0 , r = y , k = 0 . while ħalting criterion false do 1. k k + 1 2. x x ^ - τ Φ T r {take a gradient step} 3. x ^ shrink ( x , μ k τ ) {perform soft thresholding} 4. r y - Φ x ^ {update measurement residual} end while return x ^ 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:

y k + 1 = y k + y - Φ x k x k + 1 = arg min J ( x ) + μ 2 Φ x - y k + 1 2 .

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 ( x ) . In [link] , it is applied to [link] for J ( x ) = x 1 and shown to converge in a finite number of steps for any μ > 0 . For moderate μ , 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 μ , Bregman iterations are often more stable and sometimes much faster.


All the methods discussed in this section optimize a convex function (usually the 1 -norm) over a convex (possibly unbounded) set. This implies guaranteed convergence to the global optimum. In other words, given that the sampling matrix Φ satisfies the conditions specified in "Signal recovery via 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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Source:  OpenStax, An introduction to compressive sensing. OpenStax CNX. Apr 02, 2011 Download for free at http://legacy.cnx.org/content/col11133/1.5
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