Variational Analysis, Optimization, and Equilibrium Problems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C1: Difference and Differential Equations".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1773

Special Issue Editors


E-Mail Website
Guest Editor
Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: image processing; data compression; image optimization; image analysis; image classification
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Science, University of Phayao, Phayao 56000, Thailand
Interests: nonlinear and convex analysis; fixed point theory; optimization; image processing; data classification

Special Issue Information

Dear Colleagues,

Variational analysis, optimization, and equilibrium problems form a foundational triad in mathematical sciences, and these have far-reaching implications across both theory and real-world applications. These interrelated disciplines offer powerful frameworks for studying stability, convergence, and sensitivity in nonlinear systems, which are crucial in fields such as economics, engineering, data science, and machine learning. This Special Issue aims to collect original research articles that contribute to theoretical developments, algorithmic innovations, and the advancement of applied methodologies within these areas.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Variational analysis and generalized differentiation;
  • Convex and nonconvex optimization theory;
  • Variational inequalities and inclusions;
  • Equilibrium problems and quasi-equilibrium problems;
  • Fixed point theory and its applications to optimization;
  • Monotone and maximal monotone operator theory;
  • Saddle point problems and duality theory;
  • Set-valued and non-smooth analysis;
  • Proximal algorithms and operator-splitting methods;
  • Incremental and stochastic optimization methods;
  • Multiobjective and vector optimization;
  • Game theory and equilibrium modeling;
  • Numerical methods for variational and equilibrium problems;
  • Applications to machine learning, signal processing, and data science;
  • Applications in economics, finance, engineering, and network systems.

Prof. Dr. Suthep Suantai
Dr. Prasit Cholamjiak
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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • variational analysis
  • convex optimization
  • nonconvex optimization
  • bilevel optimization problems
  • variational inequality
  • equilibrium problem
  • mixed equilibrium problem
  • monotone operator theory
  • fixed point theory
  • saddle point problem
  • generalized differentiation
  • non-smooth and set-valued analysis
  • duality theory
  • proximal and operator-splitting methods
  • multi-objective and stochastic optimization
  • numerical algorithms
  • applications in machine learning and engineering
  • data classification
  • data prediction
  • image processing
  • signal processing
  • extreme learning machine
  • deep machine learning
  • non-expansive mappings
  • quasi-non-expansive mappings
  • demi-contractive mappings, family of non-expansive mappings
  • inertial technique
  • double interial algorithms
  • minimization problem
  • inclusion problems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

31 pages, 636 KB  
Article
On Bregman Asymptotically Quasi-Nonexpansive Mappings and Generalized Variational-like Systems
by Ghada AlNemer, Rehan Ali and Mohammad Farid
Mathematics 2025, 13(22), 3641; https://doi.org/10.3390/math13223641 - 13 Nov 2025
Viewed by 224
Abstract
In this work, we propose and study an inertial hybrid projection algorithm to approximate a common solution of a system of unrelated generalized mixed variational-like inequalities and the common fixed points of Bregman asymptotically quasi-nonexpansive mappings in the intermediate sense. We establish a [...] Read more.
In this work, we propose and study an inertial hybrid projection algorithm to approximate a common solution of a system of unrelated generalized mixed variational-like inequalities and the common fixed points of Bregman asymptotically quasi-nonexpansive mappings in the intermediate sense. We establish a strong convergence theorem for the generated sequence and derive several corollaries. Further, we provide applications of Bregman asymptotically quasi-nonexpansive mappings in the intermediate sense. Numerical examples are provided to demonstrate the effectiveness of the method, and we also present a comparative analysis. Full article
(This article belongs to the Special Issue Variational Analysis, Optimization, and Equilibrium Problems)
Show Figures

Figure 1

24 pages, 335 KB  
Article
A New Accelerated Forward–Backward Splitting Algorithm for Monotone Inclusions with Application to Data Classification
by Puntita Sae-jia, Eakkpop Panyahan and Suthep Suantai
Mathematics 2025, 13(17), 2783; https://doi.org/10.3390/math13172783 - 29 Aug 2025
Viewed by 701
Abstract
This paper proposes a new accelerated fixed-point algorithm based on a double-inertial extrapolation technique for solving structured variational inclusion and convex bilevel optimization problems. The underlying framework leverages fixed-point theory and operator splitting methods to address inclusion problems of the form [...] Read more.
This paper proposes a new accelerated fixed-point algorithm based on a double-inertial extrapolation technique for solving structured variational inclusion and convex bilevel optimization problems. The underlying framework leverages fixed-point theory and operator splitting methods to address inclusion problems of the form 0(A+B)(x), where A is a cocoercive operator and B is a maximally monotone operator defined on a real Hilbert space. The algorithm incorporates two inertial terms and a relaxation step via a contractive mapping, resulting in improved convergence properties and numerical stability. Under mild conditions of step sizes and inertial parameters, we establish strong convergence of the proposed algorithm to a point in the solution set that satisfies a variational inequality with respect to a contractive mapping. Beyond theoretical development, we demonstrate the practical effectiveness of the proposed algorithm by applying it to data classification tasks using Deep Extreme Learning Machines (DELMs). In particular, the training processes of Two-Hidden-Layer ELM (TELM) models is reformulated as convex regularized optimization problems, enabling robust learning without requiring direct matrix inversions. Experimental results on benchmark and real-world medical datasets, including breast cancer and hypertension prediction, confirm the superior performance of our approach in terms of evaluation metrics and convergence. This work unifies and extends existing inertial-type forward–backward schemes, offering a versatile and theoretically grounded optimization tool for both fundamental research and practical applications in machine learning and data science. Full article
(This article belongs to the Special Issue Variational Analysis, Optimization, and Equilibrium Problems)
20 pages, 1022 KB  
Article
A Double Inertial Mann-Type Method for Two Nonexpansive Mappings with Application to Urinary Tract Infection Diagnosis
by Krittin Naravejsakul, Pasa Sukson, Waragunt Waratamrongpatai, Phatcharapon Udomluck, Mallika Khwanmuang, Watcharaporn Cholamjiak and Watcharapon Yajai
Mathematics 2025, 13(15), 2352; https://doi.org/10.3390/math13152352 - 23 Jul 2025
Viewed by 506
Abstract
This study proposes a double inertial technique integrated with the Mann algorithm to address the fixed-point problem. Our method is further employed to tackle the split-equilibrium problem and perform classification using a urinary tract infection dataset in practical scenarios. The Extreme Learning Machine [...] Read more.
This study proposes a double inertial technique integrated with the Mann algorithm to address the fixed-point problem. Our method is further employed to tackle the split-equilibrium problem and perform classification using a urinary tract infection dataset in practical scenarios. The Extreme Learning Machine (ELM) model is utilized to categorize urinary tract infection cases based on both clinical and demographic features. It exhibits excellent precision and efficiency in differentiating infected from non-infected individuals. The results validate that the ELM provides a rapid and reliable method for handling classification tasks related to urinary tract infections. Full article
(This article belongs to the Special Issue Variational Analysis, Optimization, and Equilibrium Problems)
Show Figures

Figure 1

Back to TopTop