We regard a network of systems with coupled dynamics or constraints that we want to control optimally. The cost function is assumed to be separable and convex. We show that the corresponding centralized model predictive control (MPC) problem can be recast as a separable convex optimization problem. Furthermore, we present a dual-based decomposition method, called here the proximal center method...

In this paper, we propose a new method to solve the minimization problem in simultaneous reconstruction and segmentation (SRS) model for X-ray computed tomography (CT). The SRS uses Bayes' rule maximum posteriori (MAP) estimate on hidden Markov measure field (HMMFM). original [Romanov M, Dahl AB, Dong Y, Hansen PC. Simultaneous tomographic with class priors. Inverse Problems Sci Eng. 2016;24(8)...

For a given density matrix ρ of a bipartite quantum system an asymptotical separability criterion is suggested. Using the continuous ensemble method, a sequence of separable density matrices is built which converges to ρ if and only if ρ is separable. The convergence speed is evaluated and for any given tolerance parameter κ an iterative procedure is suggested which decides in finite number of ...

In this paper, we investigate the performance of introducing competition in cooperative coevolutionary algorithms to solve large-scale fully-separable continuous optimization problems. It may seem that solving large-scale fully-separable functions is trivial by means of problem decomposition. In principle, due to lack of variable interaction in fullyseparable problems, any decomposition is viab...

Separable Bayesian Networks, or the Influence Model, are dynamic Bayesian Networks in which the conditional probability distribution can be separated into a function of only the marginal distribution of a node’s parents, instead of the joint distributions. We describe the connection between an arbitrary Conditional Probability Table (CPT) and separable systems using linear algebra. We give an a...

Sažetak— This paper gives an overview of the separable two dimensional transposed Farrow structure. We analyze a separable transposed implementation structure in terms of complexity. We also present a straightforward procedure to design the separable 2-D Farrow structure. The procedure is to apply the best known one dimensional frequency domain design method for polynomial-based filters in each...

This is an overview paper written in style of research proposal. In recent years we introduced a general framework for large-scale unconstrained optimization – Sequential Subspace Optimization (SESOP) and demonstrated its usefulness for sparsity-based signal/image denoising, deconvolution, compressive sensing, computed tomography, diffraction imaging, support vector machines. We explored its co...

The split Bregman (SB) method [14] is a fast splitting-based algorithm that solves image reconstruction problems with general l1, e.g., total-variation (TV) and compressed sensing (CS), regularizations by introducing a single variable split to decouple the data-fitting term and the regularization term, yielding simple subproblems that are separable (or partially separable) and easy to minimize....