Distributed nonconvex optimization subject to globally coupled constraints via collaborative neurodynamic optimization.

In this paper, a recurrent neural network is proposed for distributed nonconvex optimization subject to globally coupled (in)equality constraints and local bound constraints. Two distributed optimization models, including a resource allocation problem and a consensus-constrained optimization problem, are established, where the objective functions are not necessarily convex, or the constraints do not guarantee a convex feasible set. To handle the nonconvexity, an augmented Lagrangian function is designed, based on which a recurrent neural network is developed for solving the optimization models in a distributed manner, and the convergence to a local optimal solution is proven.

A collaborative neurodynamic approach with two-timescale projection neural networks designed via majorization-minimization for global optimization and distributed global optimization.

In this paper, two two-timescale projection neural networks are proposed based on the majorization-minimization principle for nonconvex optimization and distributed nonconvex optimization. They are proved to be globally convergent to Karush-Kuhn-Tucker points. A collaborative neurodynamic approach leverages multiple two-timescale projection neural networks repeatedly re-initialized using a meta-heuristic rule for global optimization and distributed global optimization.

An event-triggered collaborative neurodynamic approach to distributed global optimization.

In this paper, we propose an event-triggered collaborative neurodynamic approach to distributed global optimization in the presence of nonconvexity. We design a projection neural network group consisting of multiple projection neural networks coupled via a communication network. We prove the convergence of the projection neural network group to Karush-Kuhn-Tucker points of a given global optimization problem.

A Collaborative Neurodynamic Optimization Approach to Distributed Nash-Equilibrium Seeking in Multicluster Games With Nonconvex Functions.

In this article, we propose a collaborative neurodynamic optimization (CNO) method for the distributed seeking of generalized Nash equilibriums (GNEs) in multicluster games with nonconvex functions. Based on an augmented Lagrangian function, we develop a projection neural network for the local search of GNEs, and its convergence to a local GNE is proven. We formulate a global optimization problem to which a global optimal solution is a high-quality local GNE, and we adopt a CNO approach consisting of multiple recurrent neural networks for scattering searches and a metaheuristic rule for reinitializing states.

Two-timescale recurrent neural networks for distributed minimax optimization.

In this paper, we present two-timescale neurodynamic optimization approaches to distributed minimax optimization. We propose four multilayer recurrent neural networks for solving four different types of generally nonlinear convex-concave minimax problems subject to linear equality and nonlinear inequality constraints. We derive sufficient conditions to guarantee the stability and optimality of the neural networks.

Ultrasound Radiomics Nomogram to Diagnose Sub-Centimeter Thyroid Nodules Based on ACR TI-RADS.

The aim of the present study was to develop a radiomics nomogram to assess whether thyroid nodules (TNs) < 1 cm are benign or malignant. From March 2021 to March 2022, 156 patients were admitted to the Affiliated Hospital of Nantong University, and from September 2017 to March 2022, 116 patients were retrospectively collected from the Jiangsu Provincial Hospital of Integrated Traditional Chinese and Western Medicine. These patients were divided into a training group and an external test group.

A Three-Gene Signature for Predicting the Prognosis of Patients Treated with Transarterial Chemoembolization (TACE) and Identification of PD-184352 as a Potential Drug to Reverse Nonresponse to TACE.

Background: Transarterial chemoembolization (TACE) is a first-line treatment for patients with unresectable hepatocellular carcinoma (HCC). Owing to differences in its efficacy across individuals, determining the indicators of patient response to TACE and finding approaches to reversing nonresponse thereto are necessary.

Methods: Transcriptome data were obtained from the GSE104580 dataset, in which patients were marked as having TACE response or nonresponse.

Serum alanine aminotransferase to hemoglobin ratio and radiological features predict the prognosis of postoperative adjuvant TACE in patients with hepatocellular carcinoma.

Objective: To explore the prognostic value of radiological features and serum indicators in patients treated with postoperative adjuvant transarterial chemoembolization (PA-TACE) and develop a prognostic model to predict the overall survival (OS) of patients with hepatocellular carcinoma (HCC) treated with PA-TACE.

Method: We enrolled 112 patients (75 in the training cohort and 37 in the validation cohort) with HCC treated with PA-TACE after surgical resection at the Affiliated Hospital of Nantong University between January 2012 and June 2015. The independent OS predictors were determined using univariate and multivariate regression analyses.

Identification and Validation of Necroptosis-Related LncRNA Signature in Hepatocellular Carcinoma for Prognosis Estimation and Microenvironment Status.

Hepatocellular carcinoma (HCC) is among malignancies with the highest fatality toll globally and minimal therapeutic options. Necroptosis is a programmed form of necrosis or inflammatory cell death, which can affect prognosis and microenvironmental status of HCC. Therefore, we aimed to explore the prognostic value of necroptosis-related lncRNAs (NRLs) in HCC and the role of the tumor microenvironment (TME) in immunotherapy.

Clifford-Valued Distributed Optimization Based on Recurrent Neural Networks.

In this paper, we address the Clifford-valued distributed optimization subject to linear equality and inequality constraints. The objective function of the optimization problems is composed of the sum of convex functions defined in the Clifford domain. Based on the generalized Clifford gradient, a system of multiple Clifford-valued recurrent neural networks (RNNs) is proposed for solving the distributed optimization problems.

An RNN-Based Algorithm for Decentralized-Partial-Consensus Constrained Optimization.

This technical note proposes a decentralized-partial-consensus optimization (DPCO) problem with inequality constraints. The partial-consensus matrix originating from the Laplacian matrix is constructed to tackle the partial-consensus constraints. A continuous-time algorithm based on multiple interconnected recurrent neural networks (RNNs) is derived to solve the optimization problem.

A Distributed Optimization Problem Subject to Partial-Impact Cost Functions.

This article focuses on a distributed optimization problem subject to partial-impact cost functions that relates to two decision variable vectors. To this end, two algorithms are presented with the aim of solving the considered optimization problem in a structure fashion and in a gradient fashion, respectively. Furthermore, a connection between the equilibrium of the induced algorithm and the involved optimization problem is established, with the aid of the tools from nonsmooth analysis and change of coordinate theorem.

Penalty Method for Constrained Distributed Quaternion-Variable Optimization.

This article studies the constrained optimization problems in the quaternion regime via a distributed fashion. We begin with presenting some differences for the generalized gradient between the real and quaternion domains. Then, an algorithm for the considered optimization problem is given, by which the desired optimization problem is transformed into an unconstrained setup.