Mathematical Programming Computation (MPC) publishes original research articles advancing the state of the art of practical computation in Mathematical Optimization and closely related fields. Authors are required to submit software source code and data along with their manuscripts (while open-source software is encouraged, it is not required). Where applicable, the review process will aim for verification of reported computational results. Topics of articles include:
• New algorithmic techniques, with substantial computational testing
• New applications, with substantial computational testing
• Innovative software
• Comparative tests of algorithms
• Modeling environments
• Libraries of problem instances
• Software frameworks or libraries.
Among the specific topics covered in MPC are linear programming, convex optimization, nonlinear optimization, stochastic optimization, integer programming, combinatorial optimization, global optimization, network algorithms, and modeling languages.
- Offers original research on computational issues in mathematical programming
- Article submissions are accompanied by software and data, subject to review and verification processes
- Coverage includes integer programming, linear programming, convex optimization, nonlinear programming, stochastic and robust optimization and much more
- Jonathan Eckstein
- Publishing model
- Hybrid. Open Choice – What is this?
- Downloads: 77,911 (2018)