Mathematical Programming Computation

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. MPC accepts manuscript submission from its own editorial board members in cases in which the identities of the associate editor, reviewers, and technical editor handling the manuscript can remain fully confidential. To be accepted, manuscripts submitted by editorial board members must meet the same quality standards as all other accepted submissions; there is absolutely no special preference or consideration given to such submissions.

Mathematical Programming Computation（MPC）是一本致力于推动数学优化及密切相关领域实用计算技术发展的学术期刊。该期刊鼓励作者在提交原创研究文章的同时，提供相应的软件源代码和数据，以支持开源软件的发展，虽然这不是强制性要求。审稿过程特别注重验证文章中报告的计算结果的准确性。MPC 涵盖的研究主题广泛，包括但不限于经过大量计算测试的新算法技术、新应用、创新软件、算法对比测试、建模环境、问题实例库以及软件框架或库。期刊特别关注线性规划、凸优化、非线性优化、随机优化、整数规划、组合优化、全局优化、网络算法和建模语言等领域的研究进展。 此外，MPC 在确保副主编、审稿人和技术编辑身份完全匿名的前提下，也接受编辑部成员的投稿。所有编委会成员提交的稿件都须遵循与其他稿件相同的严格质量标准，确保了评审过程的公正性和学术质量的一致性。