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Journals
Axioms
Special Issues
Data-Driven Decision Making and Optimization
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Data-Driven Decision Making and Optimization

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1435

Special Issue Editors

Dr. Sini Guo

E-Mail Website
Guest Editor
School of Management and Economics, Beijing Institute of Technology, Beijing, China
Interests: operations research; optimization; financial portfolio selection; artificial inelligence; fintech
Dr. Jiawen Gu

E-Mail Website
Guest Editor
Department of Mathematics, Southern University of Science and Technology, Shenzhen, China
Interests: financial mathematics; operations research; control and optimization
Dr. Hongguang Ma

E-Mail Website
Guest Editor
School of Economics and Management, Beijing University of Chemical Technology, Beijing, China
Interests: optimization; big data analysis; urban traffic management; logistics management

Special Issue Information

Dear Colleagues,

In the big data era, massive amounts of data are generated and collected every second. Data-driven decision making and optimization are becoming increasingly important and popular in solving practical operations research issues in finance and economics, such as financial investment decision making and intelligent logistics scheduling. Exploring the law behind the data and mining valuable information is beneficial for decision makers to make the best choice. On the other hand, massive data also increase the burden in data processing and problem solving. It is necessary and important to further study highly intelligent data-driven decision making and optimization methods to improve decision-making efficiency.

The Special Issue aims to make visible the state-of-the-art techniques in data-driven decision making and optimization methods. The proposed articles should illuminate problems related to both mathematical modeling and optimization in finance and economics fields. Original research articles and reviews are welcome.

Research areas may include (but are not limited to) the following:

  • Financial modeling and optimization;
  • Financial portfolio selection;
  • Fuzzy theory and optimization;
  • Machine learning;
  • Investment risk preferences;
  • Behavior finance;
  • Smart business models;
  • Big data analysis techniques;
  • Intelligent logistics.

We look forward to receiving your contributions.

Dr. Sini Guo
Dr. Jiawen Gu
Dr. Hongguang Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • financial portfolio selection
  • machine learning
  • artificial intelligence
  • big data
  • smart business models
  • operations research
  • control and optimization

Published Papers (1 paper)

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Research

22 pages, 740 KiB  
Article
Sensitivity Analysis of the Data Assimilation-Driven Decomposition in Space and Time to Solve PDE-Constrained Optimization Problems
by Luisa D’Amore and Rosalba Cacciapuoti
Axioms 2023, 12(6), 541; https://doi.org/10.3390/axioms12060541 - 31 May 2023
Viewed by 645
Abstract
This paper is presented in the context of sensitivity analysis (SA) of large-scale data assimilation (DA) models. We studied consistency, convergence, stability and roundoff error propagation of the reduced-space optimization technique arising in parallel 4D Variational DA problems. The results are helpful to [...] Read more.
This paper is presented in the context of sensitivity analysis (SA) of large-scale data assimilation (DA) models. We studied consistency, convergence, stability and roundoff error propagation of the reduced-space optimization technique arising in parallel 4D Variational DA problems. The results are helpful to understand the reliability of DA, to assess what confidence one can have that the simulation results are correct and to determine its configuration in any application. The main contributions of the present work are as follows. By using forward error analysis, we derived the number of conditions of the parallel approach. We found that the parallel approach reduces the number of conditions, revealing that it is more appropriate than the standard approach usually implemented in most operative software. As the background values are used as initial conditions of local PDE models, we analyzed stability with respect to time direction. Finally, we proved consistency of the proposed approach by analyzing local truncation errors of each computational kernel. Full article
(This article belongs to the Special Issue Data-Driven Decision Making and Optimization)
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