285 research outputs found
On the Effect of Boundary Conditions on the Scalability of Schwarz Methods
No full textIt is well known that one-level Schwarz methods are not weakly scalable, if the number of subdomains increases and the whole domain Ω is fixed. However, the recent work [2], published in the field of implicit solvation models used in computational chemistry, has drawn attention to the opposite case in which the number of subdomains increases, but their size remains unchanged, and, as a result, the size of the whole domain Ω increases
An Overlapping Waveform Relaxation Preconditioner for Economic Optimal Control Problems With State Constraints
No full textAs shown in [8], the semismooth Newton method lacks of convergence if the parameter is not sufficiently large. This is, however, in contrast with typical applications, where a sufficiently small is required [6, 8]. The goal of this paper is to tackle this problem by using a nonlinear preconditioning technique based on an overlapping optimized waveform-relaxation method (WRM) characterized by Robin transmission conditions [2, 3]
Convergence analysis and optimization of a Robin Schwarz waveform relaxation method for time-periodic parabolic optimal control problems
No full textThis paper is concerned with a novel convergence analysis of the optimized Schwarz waveform relaxation method (OSWRM) for the solution of optimal control problems governed by periodic parabolic partial differential equations (PDEs). The new analysis is based on a Fourier-type technique applied to a semidiscrete-in-time form of the optimality condition. This leads to a precise characterization of the convergence factor of the method at the semidiscrete level. Using this characterization, the optimal transmission condition parameter is obtained at the semidiscrete level and its asymptotic behavior as the time discretization converges to zero is analyzed in detail
History of behavioral accounting research (1960-2023): a bibliometric analysis
No full textPurpose: This study aims to examine the over 60-year evolution of behavioral accounting research (BAR), with the main aim of critically and accurately tracing its past, present and future.
Design/methodology/approach: This study used Scopus and Google Scholar databases to collect 2,263 articles of BAR published on relevant accounting journals. Thus, this study used Bibliometrix to provide a temporal overview of articles and a temporally oriented network co-occurrence analysis of BAR topics.
Findings: This study retraces the history of BAR since its origins and, also on the basis of triggering events inside (e.g. Nobel Prizes for behavioral economics studies) and outside (e.g. accounting scandals) the academic debate, this study critically discusses the evolution and interconnections of BAR topics. Then, future research is addressed toward main promising avenues, thus integrating recent technological applications into the behavioral accounting experimental designs to improve their external validity, exploring the potential positive effects of professionals’ heuristics in performing accounting tasks under certain environmental conditions, exploiting behavioral accounting frameworks to analyze and improve sustainability reporting and sustainability performance management.
Originality/value: Although BAR is rich of contributions, including sub elds and contaminations, it lacks a holistic evaluation of its origins, development and future perspectives. In this vein, to the best of the authors’ knowledge, this is the rst study to use a bibliometric analysis to evaluate the evolution of BAR
Applications of machine learning to brain disorders
No full textIn this chapter, we explore the potential applications of machine learning to brain disorders. Specifically, we illustrate why the use of machine learning in brain disorders is attracting so much interest among researchers and clinicians by highlighting three key applications: prediction of illness onset, assistance with diagnosis, and prediction of longitudinal outcomes. After illustrating these applications, we discuss the challenges that need to be overcome for a successful translational implementation of machine learning in everyday psychiatric and neurologic care. In particular, we identify three main pitfalls in the absence of biomarkers, the unreliability of clinical diagnosis, and the heterogeneity of the patients. In the final part of the chapter, we consider the requirements a machine learning algorithm needs to fulfill to be eligible for clinical use and discuss potential future directions
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