Mathematical Optimizations and Operations Research
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".
Deadline for manuscript submissions: 20 February 2025 | Viewed by 1297
Special Issue Editors
Interests: optimization method and its applications; stochasitic programming; robust optimization
Special Issue Information
Dear Colleagues,
The importance of optimization is increasing in addressing complex, real-world problems across various industries and disciplines. As the world becomes more interconnected and data-driven, the need for efficient decision-making processes that leverage mathematical optimization techniques becomes paramount.
The development of mathematical optimization has been marked by significant advancements in theoretical research and practical applications, such as nonconvex optimization algorithms, stochastic optimization, distributionally robust optimization, discrete optimization, optimization theory, and applications in artificial intelligence, transportation, and finance. However, designing efficient algorithms and applying these novel approaches to real problems is still an open issue—particularly for nonconvex optimization, nonsmooth optimization, large-scale optimization, integer programs, and optimization under uncertainty.
This Special Issue invites researchers to report their latest research on developing all aspects of mathematical optimization and new applications in operations research. The scope includes but is not limited to convex and nonconvex optimization, nonsmooth optimization, large-scale optimization, integer program, stochastic optimization, robust optimization, computational methods, and applications of optimization techniques in various domains such as finance, logistics, energy, healthcare, transportation, and manufacturing.
Dr. Shen Peng
Dr. Jia Liu
Guest Editors
Manuscript Submission Information
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Keywords
- convex and nonconvex optimization
- nonsmooth optimization
- large-scale optimization
- integer program
- stochastic optimization
- robust optimization
- distributionally robust optimization
- artificial intelligence
- operations research
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