The Journal of Optimization Theory and Applications is committed to publishing meticulously chosen, high-quality papers encompassing a range of contributions, including research papers, invited papers, survey papers, and technical notes. The content of the journal revolves around mathematical optimization techniques, computational methodologies of optimization algorithms, and their diverse applications in science, engineering, and business.
Theoretical domains covered in the journal include linear, nonlinear, conic, stochastic, discrete, and dynamic optimization, multiobjective optimization, optimal control, as well as variational and convex analysis. Application areas span mathematical economics, mathematical physics and biology, and various branches of engineering, including aerospace, biomedical, chemical, civil, electrical, and mechanical engineering. The journal encourages submissions in recent, emerging, and novel areas of optimization, such as machine/deep learning, artificial intelligence, service systems optimization, and quantum computing optimization.
The Journal of Optimization Theory and Applications embraces a comprehensive approach, particularly embracing the application of rigorous optimization techniques in solving high impact problems. It provides a platform for book notices, and forums – the latter being concise papers facilitating comments on published works, discussions on open problems, and exploration of research perspectives. Through this diverse range of contributions, the journal aims to advance optimization sciences and their applications in various scientific and engineering domains.
The special issue on "Recent Advanced techniques for nonlinear and Optimization Problems" focuses on the intersection of various techniques for nonlinear, variational and Optimization Problems by exploring cutting-edge research in methods and applications. The area encompasses a broad spectrum of topics, ranging from nonlinear and optimization problems monitoring, and performance analysis to real-world applications in various domains such as industrial, financial, and engineering. The main scope of this special issue is to provide a comprehensive platform for researchers and scientists to present original work, share insights, and contribute to the evolving landscape of nonlinear and optimization problems. The significance lies in advancing our understanding of techniques for nonlinear and optimization problems, fostering innovation on the subject, and addressing complex challenges across diverse fields.
Topics of Interest: Contributions to this special issue may include, but are not limited to, the following topics:
• Derivative free-optimization and applications
• Multi-objective and inverse multi-objective optimization.
• Operator splitting methods
• Set Optimization
• Robust optimization
• Variational Analysis in Structured Optimization
• Inertial iterative techniques for fixed point, variational inequality, and optimization problems
Guest Editors:
This special Issue is in honour of the 10th year anniversary of the Gaspard Monge Program in Optimization (PGMO) an industrially sponsored research program covering many different aspects of optimization theory. From an applicative viewpoint the program has, in particular, an interest in “energy related” problems. The latter, understood in a large sense, can be characterized by the presence of uncertainty, large scale, combinatorial aspects, nonsmoothness and non-convexity. The just mentioned features depending on the nature of the investigated models. Energy problems are of societal importance. As a result, the research in optimization carried out through PGMO can be understood to have real-life impact. In terms of mathematical optimization disciplines the scope of this SI is large as indeed a great many features can be found in problems from practice. We can thus cover:
Guest Editors:
Submission deadline: 30 June, 2024
Guest Editors:
Heinz Bauschke, University of British Columbia, heinz.bauschke@ubc.ca
Giovanni Colombo, University of Padova, Italy, colombo@math.unipd.it
René Henrion, Weierstrass Institute, henrion@wias-berlin.de
Nguyen-Truc-Dao Nguyen, University of Michigan, nntdao@umich.edu
Submission Deadline: January 15, 2024
Professor Boris Teodorovich Polyak was born on May 4, 1935 and passed away on February 3, 2023. This SI is dedicated to his memory and celebrates his fundamental achievements in Optimization, Control, and Their Applications. Professor Polyak was a world leader in these domains being a founder of several scientific schools. In particular, he was the first who thought on acceleration of the Gradient Method by suggesting a momentum idea (Heavy Ball Method). He proposed several variants of the subgradient method, which form the basis of the modern theory of Nonsmooth Optimization. His interpretation of Modified Lagrangians paved a way for proper understanding of this technique. Several, by now classical, notions of modern Optimization, such as, uniformly convex functions, gradient-dominated functions, sharp minimum, and many more, were proposed in his papers. Finally, his famous book “Introduction to Optimization” (1983) fixed the state of art for the first period in the development of Optimization Theory and highlighted the most interesting directions for further research.
Guest Editors:
Boris Mordukhovich, Wayne State University, boris@math.wayne.edu
Arkadi Nemirovski, Georgia Institute of Technology, nemirovs@isye.gatech.edu
Yurii Nesterov, Catholic University of Louvain, Belgium, nesterov@core.ucl.ac.be
Submission deadline: September 30, 2023
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