arXiv subjects

Guo-Rong Wu

Publications and source records attributed to Guo-Rong Wu.

4 recordsLinked to original sources

arXiv2017

A first-order splitting method for solving a large-scale composite convex optimization problem

The forward-backward operator splitting algorithm is one of the most important methods for solving the optimization problem of the sum of two convex functions, where one is differentiable with a Lipschitz continuous gradient and the other is possibly nonsmooth but proximable. It is convenient to solve some optimization problems in the form of dual or primal-dual problems. Both methods are mature in theory. In this paper, we construct several efficient first-order splitting algorithms for solving a multi-block composite convex optimization problem. The objective function includes a smooth function with a Lipschitz continuous gradient, a proximable convex function that may be nonsmooth, and a finite sum of a composition of a proximable function and a bounded linear operator. To solve such an optimization problem, we transform it into the sum of three convex functions by defining an appropriate inner product space. On the basis of the dual forward-backward splitting algorithm and the primal-dual forward-backward splitting algorithm, we develop several iterative algorithms that involve only computing the gradient of the differentiable function and proximity operators of related convex functions. These iterative algorithms are matrix-inversion-free and completely splitting algorithms. Finally, we employ the proposed iterative algorithms to solve a regularized general prior image constrained compressed sensing (PICCS) model that is derived from computed tomography (CT) image reconstruction under sparse sampling of projection measurements. Numerical results show that the proposed iterative algorithms outperform other algorithms.

math.OC↗

arXiv2017

A general framework for solving convex optimization problems involving the sum of three convex functions

In this paper, we consider solving a class of convex optimization problem which minimizes the sum of three convex functions $f(x)+g(x)+h(Bx)$, where $f(x)$ is differentiable with a Lipschitz continuous gradient, $g(x)$ and $h(x)$ have a closed-form expression of their proximity operators and $B$ is a bounded linear operator. This type of optimization problem has wide application in signal recovery and image processing. To make full use of the differentiability function in the optimization problem, we take advantage of two operator splitting methods: the forward-backward splitting method and the three operator splitting method. In the iteration scheme derived from the two operator splitting methods, we need to compute the proximity operator of $g+h \circ B$ and $h \circ B$, respectively. Although these proximity operators do not have a closed-form solution in general, they can be solved very efficiently. We mainly employ two different approaches to solve these proximity operators: one is dual and the other is primal-dual. Following this way, we fortunately find that three existing iterative algorithms including Condat and Vu algorithm, primal-dual fixed point (PDFP) algorithm and primal-dual three operator (PD3O) algorithm are a special case of our proposed iterative algorithms. Moreover, we discover a new kind of iterative algorithm to solve the considered optimization problem, which is not covered by the existing ones. Under mild conditions, we prove the convergence of the proposed iterative algorithms. Numerical experiments applied on fused Lasso problem, constrained total variation regularization in computed tomography (CT) image reconstruction and low-rank total variation image super-resolution problem demonstrate the effectiveness and efficiency of the proposed iterative algorithms.

math.OC↗

arXiv2013

FEL Polarization Control Studies on Dalian Coherent Light Source

The polarization switch of a free-electron laser (FEL) is of great importance to the user scientific community. In this paper, we investigate the generation of controllable polarization FEL from two well-known approaches for Dalian coherent light source, i.e., crossed planar undulator and elliptical permanent undulator. In order to perform a fair comparative study, a one-dimensional time-dependent FEL code has been developed, in which the imperfection effects of an elliptical permanent undulator are taken into account. Comprehensive simulation results indicate that the residual beam energy chirp and the intrinsic FEL gain may contribute to the degradation of the polarization performance for the crossed planar undulator. And the elliptical permanent undulator is not very sensitive to the undulator errors and beam imperfections. Meanwhile, with proper configurations of the main planar undulators and additional elliptical permanent undulator section, circular polarized FEL with pulse energy exceeds 100 $\mu$J could be achieved at Dalian coherent light source.

physics.acc-ph↗

arXiv2012

Expanding the Transfer Entropy to Identify Information Subgraphs in Complex Systems

We propose a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to informational circuits present in the system, with an informational character which can be associated to the sign of the contribution. For the sake of computational complexity, we adopt the assumption of Gaussianity and use the corresponding exact formula for the conditional mutual information. We report the application of the proposed methodology on two EEG data sets.

q-bio.QM↗