KDI: Global Adaptive Optimization for Structural Biology anand Other Complex Signal Reconstruction, Pattern Recognition and System Design Problems

KDI:结构生物学和其他复杂信号重建、模式识别和系统设计问题的全局自适应优化

基本信息

  • 批准号:
    9873139
  • 负责人:
  • 金额:
    $ 30.72万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    1999
  • 资助国家:
    美国
  • 起止时间:
    1999-01-01 至 2002-12-31
  • 项目状态:
    已结题

项目摘要

DoerschuckThe resolution of many important questions in science, engineering, and operations research requires the solution of a global optimization problem. The objective of this research is to apply state-of the art methodology in global optimization to several hard problems of scientific or engineering interest, and to develop, analyze, and implement new methodology which is capable of achieving a new level of performance on these and other complex applications.This project will focus on three areas: 1) structural biology problems, especially 3D virus reconstructions from x-ray scattering and cryo-electron microscopy data, 2) pattern classification for seismic oil exploration problems, and 3) system design and evaluation for nonlinear satellite communications links. These problems exhibit cost functions with multiple local minima and a wide variety of characteristics which complicate their optimization, including: a) cost functions that are driven by data that has a multi-scale structure, b) cost functions that are computed analytically but at great expense, c) cost functions that are computed by simulation or by a combination of analytical formulas and simulation (so-called semi-analytical simulation), and d) cost functions that are driven by changing data, where the global minimum must be tracked, possibly in real time.The first part of the work involves applying current global random search algorithms to get a baseline for the performance and computational characteristics of these methods which have not been studied extensively in the problem areas described above. The second part of the work will involve developing and analyzing algorithms with improved efficiency. Much of the practicality of global optimization stems from the extraordinary (and still growing) computation resources that are now available. The most cost-effective resources are clusters of off-the-shelf computers. This research group has access to a leading edge machine of this type. Because local searches and simulations can be carried out in parallel with minimal communication overhead, the algorithms to be developed here are ideal candidates for implementation on such computers. Implementation efforts will be focused in this direction.
要解决科学、工程和运筹学中的许多重要问题,需要解决一个全局优化问题。这项研究的目标是将最新的全局优化方法应用于几个具有科学或工程意义的难题,并开发、分析和实施能够在这些和其他复杂应用中达到新水平的新方法。本项目将集中在三个领域:1)结构生物学问题,特别是基于X射线散射和冷冻电子显微镜数据的3D病毒重建;2)地震石油勘探问题的模式分类;3)非线性卫星通信链路的系统设计和评估。这些问题展示了具有多个局部极小值的成本函数和使其优化复杂化的各种特征,包括:a)由具有多尺度结构的数据驱动的成本函数,b)通过分析计算但花费巨大的成本函数,c)通过模拟或通过分析公式和模拟的组合计算的成本函数(所谓的半解析模拟),以及d)由变化的数据驱动的成本函数,其中必须跟踪全局最小值,工作的第一部分涉及应用当前的全局随机搜索算法,以获得这些方法的性能和计算特征的基线,这些方法在上述问题领域中尚未得到广泛研究。第二部分的工作将涉及开发和分析提高效率的算法。全局优化的实用性很大程度上源于现在可用的非同寻常的(且仍在增长的)计算资源。最具成本效益的资源是现成的计算机集群。这个研究小组有机会接触到这种类型的尖端机器。由于本地搜索和模拟可以以最小的通信开销并行执行,因此这里将要开发的算法是在这样的计算机上实现的理想候选。执行工作将集中在这一方向。

项目成果

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Peter Doerschuk其他文献

Feature-Based Machine Learning for Predicting Resistances in Printed Electronics
基于特征的机器学习用于预测印刷电子产品中的电阻

Peter Doerschuk的其他文献

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{{ truncateString('Peter Doerschuk', 18)}}的其他基金

AF:CIF:Small:Computational structural biology: Reconstruction and understanding for heterogeneous biological macro molecular complexes based on electron microscopy images
AF:CIF:Small:计算结构生物学:基于电子显微镜图像的异质生物大分子复合物的重建和理解
  • 批准号:
    1217867
  • 财政年份:
    2012
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Standard Grant
Collaborative Research: CDI-Type II: Discovery of Succinct Dynamical Relationships in Large-Scale Biological Data Sets
合作研究:CDI-Type II:大规模生物数据集中简洁动态关系的发现
  • 批准号:
    0836656
  • 财政年份:
    2008
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Standard Grant
ITR: Collaborative Research: New Approaches to Experimental Design and Statistical Analysis of Genomic and Structural Biologic Data from Multiple Sources
ITR:协作研究:多源基因组和结构生物学数据的实验设计和统计分析新方法
  • 批准号:
    0735297
  • 财政年份:
    2006
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Continuing Grant
ITR: Collaborative Research: New Approaches to Experimental Design and Statistical Analysis of Genomic and Structural Biologic Data from Multiple Sources
ITR:协作研究:多源基因组和结构生物学数据的实验设计和统计分析新方法
  • 批准号:
    0325544
  • 财政年份:
    2003
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Continuing Grant
Computation for Structural Biology: Tools to Enable Dynamic 3-D Reconstruction of Time-varying Viral Structures
结构生物学计算:实现时变病毒结构动态 3D 重建的工具
  • 批准号:
    0098156
  • 财政年份:
    2001
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Standard Grant
CISE Research Resources: Computer cluster to support computational biology and other nonlinear signal reconstruction and system design problems
CISE 研究资源:支持计算生物学和其他非线性信号重建和系统设计问题的计算机集群
  • 批准号:
    0130538
  • 财政年份:
    2001
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Standard Grant
ITR/AP (BIO) Computational tools for determining the 3-D static and dynamic structure of viruses
ITR/AP (BIO) 用于确定病毒 3D 静态和动态结构的计算工具
  • 批准号:
    0112672
  • 财政年份:
    2001
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Continuing Grant
IGERT: Training Program on Therapeutic and Diagnostic devices
IGERT:治疗和诊断设备培训计划
  • 批准号:
    9972770
  • 财政年份:
    1999
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Continuing Grant
Joint 3-D Reconstruction from Cryo Electron Microscopy and Solution X-ray Scattering Data
利用冷冻电子显微镜和溶液 X 射线散射数据进行联合 3D 重建
  • 批准号:
    9630497
  • 财政年份:
    1997
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Continuing Grant
3D Reconstruction of Icosahedral Viruses from X-ray Scattering Data
根据 X 射线散射数据 3D 重建二十面体病毒
  • 批准号:
    9513594
  • 财政年份:
    1996
  • 资助金额:
    $ 30.72万
  • 项目类别:
    Continuing Grant

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