New Advances in Learning Algorithms and Optimization: Methods and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 75
Special Issue Editor
Interests: self-supervised learning; responsible AI; generative AI; deep learning; AI in healthcare; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The performance of intelligent systems is fundamentally driven by mathematical innovation in learning algorithms and optimization methods. While theoretical advances continue to push excellence, a critical challenge remains in their transformation into reliable, scalable, and impactful real-world applications. Bridging this gap is essential for society and requires new mathematical models of learning. This Special Issue invites two kinds of high-quality, original research contributions: papers that introduce novel learning and optimization algorithms with rigorous validation (highlighting their mathematical aspect) and contributions detailing the impactful application of advanced methods to solve tangible real-world problems.
Topics of interest include, but are not limited to, the following:
Scalable algorithms for large-scale data processing;
Gradient-free and black-box optimization;
Data-efficient, compute-efficient and robust learning methods;
Hybrid methods, including physics-informed learning and neuro-symbolic AI;
Practical implementations of federated and distributed learning;
Applications of reinforcement learning in robotics and control systems;
Adversarial optimization, robustness, and game-theoretic learning;
Novel optimization techniques for finance, logistics, industrial and healthcare;
Explainable AI (XAI), fairness, and bias mitigation in learning systems;
Real-world case studies demonstrating the impact of advanced algorithms;
Learning to optimize: Meta-learning and automated machine learning (AutoML).
Dr. Himanshu Buckchash
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-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
- learning algorithms
- mathematical optimization
- self-supervised learning
- real-world applications
- large-scale optimization
- black-box optimization
- algorithm engineering
- data-compute efficient learning
- adversarial optimization
- game-theoretic learning
- reinforcement learning
- data-driven problem solving
- explainable AI (XAI)
- algorithmic fairness and bias
- meta-learning
- physics-informed learning
- applied machine learning
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