This paper proposes a method for parallel block coordinate-wise minimization for convex functions. Each iteration involves a first phase where n independent minimizations are performed over the n variable blocks, followed by a phase where the results of the first phase are coordinated to obtain the whole variable update. Convergence of the method to the global optimum is proved for functions co...

Recently, there has been great interest in 3-D ultrasound imaging, but power constraints have precluded practical implementation of high-resolution and highframe-rate 3-D ultrasound in handheld imaging platforms. In this paper, we propose a separable beamforming procedure for both 3-D synthetic aperture and plane wave systems that drastically reduces computational and hence power requirements. ...

This paper shows necessary and sufficient conditions on subsets of variable exponent spaces Lp(?)(?) in order to be weakly compact. Useful criteria are given extending Andô results for Orlicz spaces. As application, we prove that all separable Banach-Saks. Also, L-weakly compact inclusions between studied.

For a type of multi-block separable convex programming raised in machine learning and statistical inference, we propose a proximal alternating direction method of multiplier (PADMM) with partially parallel splitting, which has the following nice properties: (1) To alleviate the weight of the proximal terms, the restrictions imposed on the proximal parameters are relaxed substantively; (2) To ma...

It is well-known that the least-squares identification method generally gives biased parameter estimates when the observed input-output data are corrupted with noise. If the noise acting on both the input and output is white, and if the noise variances are known, or if estimates of the noise variances are available, then the principle of biasedcompensated least-squares (CLS) can readily be used...

Dynamic contrast enhanced MRI requires high spatial resolution for morphological information and high temporal resolution for contrast pharmacokinetics. The current techniques usually have to compromise the spatial information for the required temporal resolution. This paper presents a novel method that effectively integrates sparse sampling, parallel imaging, partial separable (PS) model, and ...

In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible non-parametric ARCH model that can potentially explain the behavior of the wide variety of financial assets. The model is estimated using the generalized version of the Local Instrumental Variable Estimat...

We propose a new infeasible path-following algorithm for convex linearlyconstrained quadratic programming problem. This algorithm utilizes the monomial method rather than Newton's method for solving the KKT equations at each iteration. As a result, the sequence of iterates generated by this new algorithm is infeasible in the primal and dual linear constraints, but, unlike the sequence of iterat...

PURPOSE: Dynamic contrast enhanced (DCE) MRI requires high spatial resolution for morphological information and high temporal resolution for contrast pharmacokinetic modeling. The current fast imaging techniques [1-5] have shown success in improving the temporal resolution of DCE-MRI. However, it is still challenging to improve both the spatial and temporal resolutions simultaneously. This abst...

In this initial chapter we consider some of the basic methods used in the fitting of data by real and complex linear combinations of exponentials. We have selected the classes of methods that are most frequently used in many different fields: variable projections for solving this separable nonlinear least squares problem, derivatives and variants of Prony’s method, which rely on evenly sampled ...