Mathematical and Computational Methods for Large-Scale Optimization and Their Applications
A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 264
Editor
Interests: large scale optimization; non-convex quadratic optimization; portfolio optimization; variational inequalities; financial mathematics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Large-scale optimization problems are ubiquitous in modern science and engineering, ranging from machine learning and data analysis to structural design and logistics. As data dimensions grow exponentially, traditional methods often fail due to severe computational bottlenecks and memory constraints. This Special Issue aims to gather cutting-edge research on mathematical and computational methods specifically tailored for large-scale optimization. We invite contributions that develop novel algorithms, rigorously analyze convergence properties, and demonstrate practical efficiency in high-dimensional settings. Specific topics of interest include algorithms based on augmented Lagrangians, proximal-like methods, decomposition techniques, stochastic gradient variants, and derivative-free optimization. Submissions highlighting significant practical applications are strongly encouraged in signal processing, neural network training, and optimal control. The goal is to bridge the gap between theoretical optimization advances and real-world computational challenges, fostering collaboration between mathematicians and computational scientists. We particularly welcome studies addressing non-convexity, distributed computing environments, and robustness against noise. Researchers are encouraged to submit original work that combines theoretical depth with computational validation. We particularly welcome case studies demonstrating tangible impact in critical sectors such as finance, healthcare, and sustainable energy systems. This collection will serve as a reference for state-of-the-art methodologies.
Contributions may include, but are not limited to, the following topics:
- Algorithms based on augmented Lagrangians;
- Alternating Direction Method of Multipliers (ADMM);
- Proximal-like methods and splitting algorithms;
- Stochastic and distributed optimization techniques;
- Optimization in machine learning and deep neural networks;
- Derivative-free optimization for black-box problems;
- Applications in engineering, economics, and data science.
Prof. Dr. Abdelouahed Hamdi
Guest Editor
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-anonymized 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
- large-scale optimization
- numerical algorithms
- machine learning
- convex analysis
- scientific computing
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