Maryam Yashtini

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This paper develops a Bregman operator splitting algorithm with variable stepsize (BOSVS) for solving problems of the form min{φ(Bu)+ 1/2‖Au− f ‖2}, where φ may be nonsmooth. The original Bregman Operator Splitting (BOS) algorithm employed a fixed stepsize, while BOSVS uses a line search to achieve better efficiency. These schemes are applicable to total(More)
In this paper, we propose a recurrent neural network model for solving a class of monotone variational inequalities problem with linear constraints. The neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution. Compared with the existing convergence results, the present proof do not require Lipschitz continuity(More)
Analternating direction approximateNewton (ADAN)method is developed for solving inverse problems of the form min{φ(Bu)+ (1/2)‖Au− f ‖2}, where φ is convex and possibly nonsmooth, and A and B are matrices. Problems of this form arise in image reconstruction where A is the matrix describing the imaging device, f is the measured data, φ is a regularization(More)
Abstract. An earlier paper proved the convergence of a variable stepsize Bregman operator splitting algorithm (BOSVS) for minimizing φ(Bu) + H(u) where H and φ are convex functions, and φ is possibly nonsmooth. The algorithm was shown to be relatively efficient when applied to partially parallel magnetic resonance image reconstruction problems. In this(More)
A new algorithm is presented for efficiently solving image reconstruction problems that arise in partially parallel magnetic resonance imaging. This algorithm minimizes an objective function of the form &#x03C6;(Bu) + 1/2||F<sub>p</sub>Su - f||<sup>2</sup>, where &#x03C6; is the regularization term which may be nonsmooth. In image reconstruction, the(More)
A new neural network model is proposed for solving nonlinear optimization problems with a general form of linear constraints. Linear constraints, which may include equality, inequality and bound constraints, are considered to cover the need for engineering applications. By employing this new model in image fusion algorithm, an optimal fusion vector is(More)
An earlier paper proved the convergence of a variable stepsize Bregman operator splitting algorithm (BOSVS) for minimizing φ(Bu) + H(u), where H and φ are convex functions, and φ is possibly nonsmooth. The algorithm was shown to be relatively efficient when applied to partially parallel magnetic resonance image reconstruction problems. In this paper, the(More)