Let x ∈ C be a spectrally sparse signal consisting of r complex sinusoids with or without damping. We consider the spectral compressed sensing problem, which is about reconstructing x from its partial revealed entries. By utilizing the low rank structure of the Hankel matrix corresponding to x, we develop a computationally efficient algorithm for this problem. The algorithm starts from an initi...

In recent years, convex optimization methods were successfully applied for various image processing tasks and a large number of first-order methods were designed to minimize the corresponding functionals. Interestingly, it was shown recently in [25] that the simple idea of so-called “superstep cycles” leads to very efficient schemes for time-dependent (parabolic) image enhancement problems as w...

Over the last two decades, it has been observed that using the gradient vector as a search direction in large-scale optimization may lead to efficient algorithms. The effectiveness relies on choosing the step lengths according to novel ideas that are related to the spectrum of the underlying local Hessian rather than related to the standard decrease in the objective function. A review of these ...

A practical active-set method for bound-constrained minimization is introduced. Within the current face the classical Euclidian trust-region method is employed. Spectral projected gradient directions are used to abandon faces. Numerical results are presented.

We investigate a constrained version of simultaneous iterative reconstruction techniques (SIRT) from the general viewpoint of projected gradient methods. This connection enable us to assess the computational merit of this algorithm class. We borrow a leaf from numerical optimization to cope with the slow convergence of projected gradient methods and propose an acceleration procedure based on th...

Many-body dynamics problems are expected to handle millions of unknowns when, for instance, investigating the three-dimensional flow of granular material. Unfortunately, the size of the problems tractable by existing numerical solution techniques is severely limited on convergence grounds. This is typically the case when the equations of motion embed a differential variational inequality (DVI) ...

Many-body dynamics problems are expected to handle millions of unknowns when, for instance, investigating the three-dimensional flow of granular material. Unfortunately, the size of the problems tractable by existing numerical solution techniques is severely limited on convergence grounds. This is typically the case when the equations of motion embed a differential variational inequality (DVI) ...