Theory and Efficient Algorithms for Hard, Large Scale, Numerical Optimization

大规模硬数值优化的理论和高效算法

基本信息

  • 批准号:
    9161-2013
  • 负责人:
  • 金额:
    $ 2.48万
  • 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:
    2014
  • 资助国家:
    加拿大
  • 起止时间:
    2014-01-01 至 2015-12-31
  • 项目状态:
    已结题

项目摘要

The focus of my research will be the proper modelling of hard problems, and the design and implementation of efficient and robust numerical algorithms for large scale, hard, optimization problems. The problems I will deal with arise in many important applications, e.g. molecular conformation (MC), sensor network localization (SNL), inverse imaging and machine learning. In particular, many of these problems arise in the relaxations of hard combinatorial optimization problems. In many instances, the usual modelling approaches result in problems that are both large scale and ill-posed. Therefore, they are hard to solve numerically. Rather than being a disadvantage, one can often take advantage of the ill-posedness to get both a stable problem and one that is smaller in size. In particular, for problems such as SNL one can solve huge problems to high accuracy by exploiting the hidden degeneracy. I plan on applying this technique to MC problems with noisy data as well as to protein design problems.
我的研究重点将是对困难问题进行适当的建模,以及为大规模、困难的优化问题设计和实现高效和健壮的数值算法。我将要处理的问题出现在许多重要的应用中,例如分子构象(MC)、传感器网络定位(SNL)、逆成像和机器学习。特别是,这些问题中的许多都是在困难的组合优化问题的松弛下出现的。在许多情况下,通常的建模方法会导致大规模和不适定的问题。因此,它们很难在数值上求解。与其成为劣势,人们往往可以利用这种不适定性,既得到一个稳定的问题,又得到一个规模较小的问题。特别是,对于像SNL这样的问题,人们可以利用隐藏的简并性来高精度地解决巨大的问题。我计划将这一技术应用于带有噪声数据的MC问题以及蛋白质设计问题。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Wolkowicz, Henry其他文献

On Equivalence of Semidefinite Relaxations for Quadratic Matrix Programming
二次矩阵规划半定松弛的等价
  • DOI:
    10.1287/moor.1100.0473
  • 发表时间:
    2011-02
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    Ding, Yichuan;Ge, Dongdong;Wolkowicz, Henry
  • 通讯作者:
    Wolkowicz, Henry
Low-rank matrix completion using nuclear norm minimization and facial reduction
  • DOI:
    10.1007/s10898-017-0590-1
  • 发表时间:
    2018-09-01
  • 期刊:
  • 影响因子:
    1.8
  • 作者:
    Huang, Shimeng;Wolkowicz, Henry
  • 通讯作者:
    Wolkowicz, Henry
Facial reduction for symmetry reduced semidefinite and doubly nonnegative programs.
  • DOI:
    10.1007/s10107-022-01890-9
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    Hu, Hao;Sotirov, Renata;Wolkowicz, Henry
  • 通讯作者:
    Wolkowicz, Henry
Sensor Network Localization, Euclidean Distance Matrix completions, and graph realization
  • DOI:
    10.1007/s11081-008-9072-0
  • 发表时间:
    2010-02-01
  • 期刊:
  • 影响因子:
    2.1
  • 作者:
    Ding, Yichuan;Krislock, Nathan;Wolkowicz, Henry
  • 通讯作者:
    Wolkowicz, Henry
Robust Interior Point Method for Quantum Key Distribution Rate Computation
  • DOI:
    10.22331/q-2022-09-08-792
  • 发表时间:
    2022-09-01
  • 期刊:
  • 影响因子:
    6.4
  • 作者:
    Hu, Hao;Im, Jiyoung;Wolkowicz, Henry
  • 通讯作者:
    Wolkowicz, Henry

Wolkowicz, Henry的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Wolkowicz, Henry', 18)}}的其他基金

Exploiting Structure and Hidden Convexity in Hard, Large Scale Numerical Optimization
在困难的大规模数值优化中利用结构和隐藏凸性
  • 批准号:
    RGPIN-2018-04028
  • 财政年份:
    2022
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Exploiting Structure and Hidden Convexity in Hard, Large Scale Numerical Optimization
在困难的大规模数值优化中利用结构和隐藏凸性
  • 批准号:
    RGPIN-2018-04028
  • 财政年份:
    2021
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Exploiting Structure and Hidden Convexity in Hard, Large Scale Numerical Optimization
在困难的大规模数值优化中利用结构和隐藏凸性
  • 批准号:
    RGPIN-2018-04028
  • 财政年份:
    2020
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Exploiting Structure and Hidden Convexity in Hard, Large Scale Numerical Optimization
在困难的大规模数值优化中利用结构和隐藏凸性
  • 批准号:
    RGPIN-2018-04028
  • 财政年份:
    2019
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Exploiting Structure and Hidden Convexity in Hard, Large Scale Numerical Optimization
在困难的大规模数值优化中利用结构和隐藏凸性
  • 批准号:
    RGPIN-2018-04028
  • 财政年份:
    2018
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Theory and Efficient Algorithms for Hard, Large Scale, Numerical Optimization
大规模硬数值优化的理论和高效算法
  • 批准号:
    9161-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Theory and Efficient Algorithms for Hard, Large Scale, Numerical Optimization
大规模硬数值优化的理论和高效算法
  • 批准号:
    9161-2013
  • 财政年份:
    2016
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Theory and Efficient Algorithms for Hard, Large Scale, Numerical Optimization
大规模硬数值优化的理论和高效算法
  • 批准号:
    9161-2013
  • 财政年份:
    2015
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Workshop on Nonlinear Optimization Algorithms and Industrial Applications
非线性优化算法及工业应用研讨会
  • 批准号:
    491740-2015
  • 财政年份:
    2015
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Regional Office Discretionary Funds
Theory and Efficient Algorithms for Hard, Large Scale, Numerical Optimization
大规模硬数值优化的理论和高效算法
  • 批准号:
    9161-2013
  • 财政年份:
    2013
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual

相似海外基金

CIF: Small: Theory and Algorithms for Efficient and Large-Scale Monte Carlo Tree Search
CIF:小型:高效大规模蒙特卡罗树搜索的理论和算法
  • 批准号:
    2327013
  • 财政年份:
    2023
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Standard Grant
CAREER: Efficient Uncertainty Quantification in Turbulent Combustion Simulations: Theory, Algorithms, and Computations
职业:湍流燃烧模拟中的高效不确定性量化:理论、算法和计算
  • 批准号:
    2143625
  • 财政年份:
    2022
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Continuing Grant
Towards more efficient machine learning algorithms: theory and practice
迈向更高效的机器学习算法:理论与实践
  • 批准号:
    RGPIN-2016-05942
  • 财政年份:
    2021
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
CAREER: Theory and Algorithms for Efficient Control of Wireless Networks with Jointly Optimized Performance: High Throughput, Low Delay, and Low Complexity
职业:具有联合优化性能的无线网络高效控制的理论和算法:高吞吐量、低延迟和低复杂性
  • 批准号:
    2112694
  • 财政年份:
    2020
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Continuing Grant
Towards more efficient machine learning algorithms: theory and practice
迈向更高效的机器学习算法:理论与实践
  • 批准号:
    RGPIN-2016-05942
  • 财政年份:
    2020
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Towards more efficient machine learning algorithms: theory and practice
迈向更高效的机器学习算法:理论与实践
  • 批准号:
    RGPIN-2016-05942
  • 财政年份:
    2019
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Towards more efficient machine learning algorithms: theory and practice
迈向更高效的机器学习算法:理论与实践
  • 批准号:
    RGPIN-2016-05942
  • 财政年份:
    2018
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Theory and Efficient Algorithms for Hard, Large Scale, Numerical Optimization
大规模硬数值优化的理论和高效算法
  • 批准号:
    9161-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
Towards more efficient machine learning algorithms: theory and practice
迈向更高效的机器学习算法:理论与实践
  • 批准号:
    RGPIN-2016-05942
  • 财政年份:
    2017
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Discovery Grants Program - Individual
CAREER: Theory and Algorithms for Efficient Control of Wireless Networks with Jointly Optimized Performance: High Throughput, Low Delay, and Low Complexity
职业:具有联合优化性能的无线网络高效控制的理论和算法:高吞吐量、低延迟和低复杂性
  • 批准号:
    1651947
  • 财政年份:
    2017
  • 资助金额:
    $ 2.48万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了