Mathematical Programming - Springer - Impact Factor

About: Mathematical Programming is a renowned journal published by Springer, dedicated to the field of optimization and mathematical programming. The journal serves as a comprehensive platform for researchers, practitioners, and academicians to present their latest findings and advancements in mathematical programming and optimization theory.
Publication Types:
Research Articles: Detailed papers presenting original research contributions in mathematical programming and optimization.
Survey Papers: Comprehensive reviews that summarize the current state-of-the-art in specific areas of mathematical programming.
Short Communications: Brief reports on significant findings or novel methodologies in the field.
Case Studies: Papers that demonstrate the application of optimization techniques to real-world problems.
Impact and Contribution:
Mathematical Programming journal plays a pivotal role in advancing the field of optimization and mathematical programming. It provides a rigorous platform for the dissemination of high-quality research, fostering innovation, and facilitating the exchange of ideas among researchers and practitioners. The journal contributions are instrumental in driving advancements in optimization theory and its applications across diverse domains.

Scope: Mathematical Programming is an international journal dedicated to publishing high-quality research articles, surveys, and reports on mathematical optimization. The journal serves as a platform for the dissemination of significant theoretical and practical advances in the field of optimization and mathematical programming.
The scope of the journal includes, but is not limited to, the following areas:
Theoretical Foundations:
Linear programming
Nonlinear programming
Integer programming
Mixed-integer programming
Convex and non-convex optimization
Stochastic programming
Combinatorial optimization
Global optimization
Optimization under uncertainty
Algorithm Development:
Development of new optimization algorithms
Analysis of algorithmic performance
Computational complexity of optimization problems
Heuristic and metaheuristic methods
Exact and approximation algorithms
Parallel and distributed algorithms for optimization
Applications:
Real-world applications in engineering, economics, and the sciences
Optimization in logistics and supply chain management
Financial optimization
Energy systems optimization
Network optimization
Machine learning and data-driven optimization
Multi-objective optimization