Horn Minimization by Iterative Decomposition 1

Bregmanized Domain Decomposition for Image Restoration

Computational problems of large-scale appearing in biomedical imaging, astronomy, art restoration, and data analysis are gaining recently a lot of attention due to better hardware, higher dimensionality of images and data sets, more parameters to be measured, and an increasing number of data acquired. In the last couple of years non-smooth minimization problems such as total variation minimizat...

Numerical solution of Klein-Gordon equation by using the Adomian's decomposition and variational iterative methods

Finding the polar decomposition of a matrix by an efficient iterative method

Theobjective in this paper to study and present a new iterative method possessing high convergence order for calculating the polar decompostion of a matrix. To do this, it is shown that the new scheme is convergent and has high convergence. The analytical results are upheld via numerical simulations and comparisons.

Fixed Point and Bregman Iterative Methods for Matrix Rank Minimization

The linearly constrained matrix rank minimization problem is widely applicable in many fields such as control, signal processing and system identification. The tightest convex relaxation of this problem is the linearly constrained nuclear norm minimization. Although the latter can be cast as a semidefinite programming problem, such an approach is computationally expensive to solve when the matr...

On Approximate Horn Minimization

The minimization problem for Horn formulas is to find a Horn formula equivalent to a given Horn formula, using a minimum number of clauses. A 2 1−ǫ(n)-inapproximability result is proven, which is the first non-trivial inapproximability result for this problem. We also consider several other versions of Horn minimization. The more general version which allows for the introduction of new variable...