Aims and scope

The Journal of Global Optimization publishes carefully refereed papers that encompass theoretical, computational, and applied aspects of global optimization. While the focus is on original research contributions dealing with the search for global optima of non-convex, multi-extremal problems, the journal’s scope covers optimization in the widest sense, including nonlinear, mixed integer, combinatorial, stochastic, robust, multi-objective optimization, computational geometry, and equilibrium problems. Relevant works on data-driven methods and optimization-based data mining are of special interest.

In addition to papers covering theory and algorithms of global optimization, the journal publishes significant papers on numerical experiments, new testbeds, and applications in engineering, management, and the sciences. Applications of particular interest include healthcare, computational biochemistry, energy systems, telecommunications, and finance. Apart from full-length articles, the journal features short communications on both open and solved global optimization problems. It also offers reviews of relevant books and publishes special issues.

Special Issue on Mathematical Optimization Theory and Applications: ICMOTA 2026
截稿日期: 2027-07-31

This special issue is devoted to research papers that extend and deepen selected contributions from the International Conference on Mathematical Optimization Theory and Applications (ICMOTA 2026), held at the Indian Institute of Technology (BHU) Varanasi, India. We welcome manuscripts presenting theoretical or computational advances, as well as application-oriented studies where global, nonconvex, or large-scale optimization plays a central role. Submitted manuscripts should contain a substantial mathematical contribution to the theory, algorithms, or applications of optimization. While this issue is primarily intended for ICMOTA 2026 contributors, high-quality submissions from researchers worldwide are warmly encouraged.

Topics of interest include, but are not limited to: Global optimization, stochastic optimization, robust optimization, multi-objective optimization, optimization for statistical models, and optimization for data science.

Guest editors:

Minh N. Dao (RMIT University, Australia), minh.dao@rmit.edu.au
Debdas Ghosh (Indian Institute of Technology (BHU) Varanasi, India), debdas.mat@iitbhu.ac.in  
V. Vetrivel (Indian Institute of Technology Madras, India), vetri@iitm.ac.in

https://link.springer.com/journal/10898/updates/27848620