Computationally Aggressive Approaches to Adaptive Design

自适应设计的积极计算方法

• 批准号: 0072910
• 负责人: Quentin Stout
• 金额: $ 24.7万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0072910&HistoricalAwards=false
• 关键词: Computationally; Aggressive; Approaches; Adaptive; Design

AbstractThis project involves interdisciplinary research in which algorithmicapproaches are developed to design and analyze adaptive experiments. Anadaptive (sequential) design is one whose characteristics change inaccordance with information arising from the ongoing experiment, as opposed to classical statistical designs where such characteristics areset in advance and remain fixed throughout. Adaptive designs have a widerange of application in clinical trials, destructive testing, behavioralecology, computer performance prediction, adaptive control, etc., wherethey have the potential to reduce the expenditure of experimental ``resources'' such as time, money, or quality of life. Unfortunately,adaptive designs are difficult to analyze and optimize. Exact analyticsolutions are rarely available, and thus, historically, such designs havebeen predominantly approached via asymptotic methods and ad hocapproximations. Computationally, adaptive designs require significanttime and space that has often made exact calculations infeasible.This project will expand the size and scope of solvable problems bydeveloping new computational approaches for creating and evaluatingdesigns and utilizing state of the art computational facilities.Attention is directed to problems that are important in applications,with a major emphasis on supplying researchers greater flexibility inmodeling their statistical and cost objectives. For many of theseproblems, exact optimality will be unattainable, and thus techniques forproducing near-optimal designs will also be pursued. Several of thesetechniques are based on optimizing smaller or simpler problems andextrapolating their solution structure to larger or more complexproblems. This compliments analytical, asymptotic work and provides newinsights into the structure of solutions. In other cases, a shift fromserial algorithms to parallel ones will be used to address theadditional complexity.

这个项目涉及跨学科的研究，其中开发了设计和分析适应性实验的算法方法。自适应(序贯)设计是一种特征根据正在进行的实验中产生的信息而变化的设计，而不是传统的统计设计，在传统的统计设计中，这些特征是预先设置的，并在整个过程中保持不变。自适应设计在临床试验、破坏性测试、行为生态学、计算机性能预测、自适应控制等方面有着广泛的应用，它们有可能减少时间、金钱或生活质量等实验“资源”的支出。不幸的是，适应性设计很难分析和优化。精确的解析解很少，因此，从历史上看，这种设计主要是通过渐近方法和近似方法来实现的。在计算方面，自适应设计需要大量的时间和空间，这往往使精确的计算变得不可行。这个项目将通过开发新的计算方法来创建和评估设计并利用最先进的计算设施来扩大可解决问题的规模和范围。关注在应用中重要的问题，主要重点是为研究人员提供更大的灵活性来模拟他们的统计和成本目标。对于许多这样的问题，精确的最优化将是无法实现的，因此也将追求产生接近最优设计的技术。其中一些技术是基于优化较小或较简单的问题，并将其解结构推演为较大或较复杂的问题。这是对分析性、渐近性工作的赞扬，并为了解的结构提供了新的见解。在其他情况下，从串行算法到并行算法的转变将被用来解决额外的复杂性。