Emerging Research in Optimization and Machine Learning

Dear Colleagues,

We are pleased to invite you to submit your research to be considered for the Special Issue “Emerging Research in Optimization and Machine Learning”. This Special Issue aims to present research developments on the dynamic interplay between these two pivotal fields. In an era where artificial intelligence and computational methods are revolutionizing industries and scientific disciplines, understanding how optimization techniques enhance machine learning algorithms—and vice versa—is of paramount importance.

This Special Issue aims to showcase the latest advancements across a spectrum of topics, including mathematical programming, nonsmooth optimization, constrained optimization, optimization in deep learning, convex and nonconvex optimization, metaheuristics, multi-objective optimization, methods for regularized models, stochastic optimization, optimization in reinforcement learning, support vector machines, and clustering. We also invite contributions on applications of these topics to model systems in physics, chemistry, biology, and engineering.

We invite researchers to submit original research articles, reviews, and perspectives that explore the symbiosis between optimization and machine learning. Whether you are developing novel optimization methodologies tailored for machine learning tasks, integrating optimization principles into machine learning frameworks, or unveiling applications that harness the power of this symbiotic relationship, we welcome your contributions.

Through this Special Issue, we seek to foster collaboration, inspire innovation, and advance the collective understanding of optimization and machine learning.

Topics of interest include (but are not limited to) the following:

• Mathematical programming;
• Nonsmooth optimization;
• Constrained optimization;
• Optimization in deep learning;
• Convex and nonconvex optimization;
• Metaheuristics;
• Multi-objective optimization;
• Methods for regularized models;
• Stochastic optimization;
• Optimization in reinforcement learning;
• Support vector machines;
• Clustering.

Dr. Maria Fernanda Pires da Costa
Dr. Luís L. Ferrás
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. Information 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 1600 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.

• machine learning
• nonlinear optimization
• nonsmooth optimization
• convex optimization
• nonconvex optimization
• constrained optimization
• stochastic optimization
• deep learning
• reinforcement learning
• SVM
• clustering