Computational, Combinatorial, and Geometric Aspects of Linear Optimization

线性优化的计算、组合和几何方面

• 批准号: RGPIN-2015-06163
• 负责人: Deza, Antoine
• 金额: $ 2.04万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=590884
• 关键词: Computational; Combinatorial; Geometric; Aspects; Linear

Rational decision-making through quantitative modelling and analysis is the guiding principle behind operations research, a field with several far-reaching applications across engineering, sciences, and industry. Finding optimal allocations of resources, scheduling tasks, and designing prototypes are a few of the areas operations research is concerned with. These problems can often be formulated, or approximated, as linear optimization problems, which involve maximizing or minimizing a linear function over a domain defined by a set of linear inequalities. The simplex and primal-dual interior point methods are currently the most computationally successful algorithms for linear optimization.

通过定量建模和分析，合理的决策是运营研究背后的指导原则，该领域在工程，科学和行业中具有多个深远的应用程序。研究资源，调度任务和设计原型的最佳分配是操作研究的一些领域。这些问题通常可以作为线性优化问题提出或近似为线性优化问题，这些问题涉及在由一组线性不平等的域上最大化或最小化线性函数。当前，单纯形和原始的双重内部点方法是线性优化的计算成功算法。