Continuous Optimization
The range of subjects covered under continuous optimization includes standard topics such as linear, nonlinear, conic, and semi-definite programming and methods. Additionally, this section typically handles articles applying the tools of convex, nonsmooth, and variational analysis to mathematical programming models, variational inequalities, and problems of control and game theory. We particularly welcome novel theoretical developments as well as new applications of mathematical programming to the natural sciences, engineering, economics, and computer science.
Discrete Optimization
The Discrete Optimization section welcomes high-quality papers on the theory and algorithms of combinatorial and discrete optimization. We particularly encourage contributions that advance the mathematical foundations of discrete optimization, including polyhedral theory, complexity and approximation results, and the design and analysis of exact and heuristic algorithms. Papers combining rigorous theoretical insights with algorithmic innovation and careful computational validation are especially welcome. The section also considers work that bridges discrete and continuous optimization, in close coordination with the Continuous Optimization section. Applications of discrete optimization methods, including emerging connections to data-driven optimization, are also welcome when they provide clear methodological or conceptual insights.
Optimization under Uncertainty
Optimization under Uncertainty section welcomes papers on all aspects of stochastic optimization, including but not limited to stochastic programming, robust optimization, distributionally robust optimization, Markov decision processes, and contextual stochastic optimization. Submissions may focus on modeling and theoretical developments as well as algorithmic and computational aspects. In addition, well-motivated applications that demonstrate the impact of optimization under uncertainty in real-world settings are welcome. Application papers should clearly articulate the sources of uncertainty, justify modeling choices, and provide empirical evidence of performance improvements. We especially encourage contributions that integrate optimization with statistics, machine learning, and related fields, particularly those that advance data-driven optimization under uncertainty.
Optimization and Machine Learning
The Optimization and Machine Learning section welcomes papers on the interplay between mathematical optimization and machine learning. For instance we encourage contributions on the use of optimization methods in machine learning, as well as contributions showing how learning-based approaches can enhance the modeling, analysis, and solution of optimization problems. In general, submissions to this area should articulate their relevance to optimization and explain why the contribution is significant beyond a narrow technical specialty. Papers centered primarily on empirical performance, without substantial foundational contribution, are unlikely to be a good fit for this area. In contrast, articles combining theoretical insight, algorithmic novelty, and computational evidence are especially welcome.