Discrete Optimization: From Applications to Relaxations

离散优化:从应用到松弛

基本信息

  • 批准号:
    RGPIN-2015-06746
  • 负责人:
  • 金额:
    $ 3.64万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2018
  • 资助国家:
    加拿大
  • 起止时间:
    2018-01-01 至 2019-12-31
  • 项目状态:
    已结题

项目摘要

This is  a research proposal on optimization, an area which crosses several disciplines: pure and applied mathematics, computer science, operations research and engineering. Mathematical optimization is thriving in part due to a wealth of applications. In addition to traditional logistic outlets such as inventory management and scheduling, new applications put demands on optimization methodology to cope with very large scale data sets and the desire for real-time answers. Additional applications seek new models which incorporate game-theoretic strategy into the optimizer's objective. Virtually all online retailers, for instance, are leveraging optimization tools in various aspects of their operations (fulfilment, shipping, revenue management). There is a corresponding demand for new personnel in the area to meet a seemingly insatiable appetite for expertise in machine learning, analytics and data science, areas which are strong users of discrete and continuous optimization.***Research directions proposed range from purely theoretical and structural topics in integer programming to modelling and empirical questions well-motivated by practical considerations in data networks. Considerable emphasis is placed on further development of robust optimization, one approach for  coping with optimization under uncertainty. The PI has also recently worked in online algorithms, which can be viewed as another model dealing with uncertainty. A recurring argument in the proposal is that harnessing the power of optimization for real-world problems is improved when it is informed by the underlying theoretical principles.  Thus, apart from presenting several computational activities, a number of  challenging medium/long-term  theoretical objectives are identified. These include mathematical questions about flow-cut gaps and sparsest cut, such as when is a finite metric embeddable into L_1 with constant distortion?, to when do all lattice points in a parallelepiped lie on a hyperplane?*** **
这是一个关于优化的研究建议,这个领域跨越了几个学科:纯数学和应用数学,计算机科学,运筹学和工程学。数学优化之所以蓬勃发展,部分原因在于它的大量应用。除了库存管理和调度等传统的物流出口外,新的应用对优化方法提出了要求,以科普非常大规模的数据集和对实时答案的需求。其他应用程序寻求新的模式,将博弈论的战略到优化器的目标。 例如,几乎所有的在线零售商都在其运营的各个方面(履行,运输,收入管理)利用优化工具。相应地,该领域也需要新的人才,以满足人们对机器学习、分析和数据科学专业知识似乎永不满足的需求,这些领域都是离散和连续优化的强大用户。提出的研究方向范围从整数规划中的纯理论和结构性主题到数据网络中实际考虑的建模和经验问题。相当大的重点放在进一步发展的鲁棒优化,一种方法来处理优化下的不确定性。PI最近也在在线算法中工作,这可以被视为处理不确定性的另一种模型。该提案中反复出现的一个论点是,利用优化的力量来解决现实世界的问题,当它被基本的理论原则所告知时,就会得到改善。因此,除了提出几个计算活动外,还确定了一些具有挑战性的中长期理论目标。这些问题包括关于流割间隙和稀疏割的数学问题,例如何时有限度量可嵌入L_1且具有常数失真?什么时候平行六面体中的所有格点都位于超平面上?*** **

项目成果

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Shepherd, Bruce其他文献

Shepherd, Bruce的其他文献

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{{ truncateString('Shepherd, Bruce', 18)}}的其他基金

New Synergies Between Combinatorial and Continuous Optimization
组合优化和连续优化之间的新协同作用
  • 批准号:
    RGPIN-2020-06141
  • 财政年份:
    2022
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
New Synergies Between Combinatorial and Continuous Optimization
组合优化和连续优化之间的新协同作用
  • 批准号:
    RGPIN-2020-06141
  • 财政年份:
    2021
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
New Synergies Between Combinatorial and Continuous Optimization
组合优化和连续优化之间的新协同作用
  • 批准号:
    RGPIN-2020-06141
  • 财政年份:
    2020
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
Discrete Optimization: From Applications to Relaxations
离散优化:从应用到松弛
  • 批准号:
    RGPIN-2015-06746
  • 财政年份:
    2019
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
Discrete Optimization: From Applications to Relaxations
离散优化:从应用到松弛
  • 批准号:
    RGPIN-2015-06746
  • 财政年份:
    2017
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
Discrete Optimization: From Applications to Relaxations
离散优化:从应用到松弛
  • 批准号:
    RGPIN-2015-06746
  • 财政年份:
    2017
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
Discrete Optimization: From Applications to Relaxations
离散优化:从应用到松弛
  • 批准号:
    RGPIN-2015-06746
  • 财政年份:
    2016
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
Discrete Optimization: From Applications to Relaxations
离散优化:从应用到松弛
  • 批准号:
    RGPIN-2015-06746
  • 财政年份:
    2015
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
Polyhedral methods for optimization and algorithm design
用于优化和算法设计的多面体方法
  • 批准号:
    342457-2010
  • 财政年份:
    2014
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual
Polyhedral methods for optimization and algorithm design
用于优化和算法设计的多面体方法
  • 批准号:
    342457-2010
  • 财政年份:
    2013
  • 资助金额:
    $ 3.64万
  • 项目类别:
    Discovery Grants Program - Individual

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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
  • 批准号:
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  • 资助金额:
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Algorithms for some hard discrete nonlinear optimization problems and applications
一些硬离散非线性优化问题的算法及应用
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  • 资助金额:
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无线网络应用的离散优化
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无线网络应用的离散优化
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    2017
  • 资助金额:
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  • 资助金额:
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