SHF: Medium: Collaborative Research: Marrying program analysis and numerical search
SHF:媒介:协作研究:结合程序分析和数值搜索
基本信息
- 批准号:1161775
- 负责人:
- 金额:$ 60万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2012
- 资助国家:美国
- 起止时间:2012-09-01 至 2016-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This research project explores ways to solve optimization problems where the targets of optimization are programs containing general-purpose control and data constructs. Such optimization questions arise often in the everyday practice of software engineering. While it may seem that standard optimization packages could solve these problems, it is often not so. White-box optimization approaches like linear programming are ruled out here because they only permit very restricted classes of objective functions. Black-box optimization techniques like gradient descent and Nelder-Mead search are applicable in principle, but they work well only in relatively smooth search spaces, and due to arbitrarily nested branches and loops, even simple programs can have highly irregular, ill-conditioned behavior.The central insight guiding this project is that program analysis techniques from the field of formal reasoning about programs can work together with blackbox optimization toolkits, and make it possible to solve many more problems of the above sort than are currently possible. Ultimately, the project will produce a unified system for optimizing programs that can leverage flexible combinations of optimization techniques and program analysis strategies. As numerous real-world problems faced in the development of everyday software are optimization problems, this system will offer a new range of capabilities to the end programmer. In addition, the research will foster synergy between two different research areas customarily housed in different academic departments.
该研究项目探索解决优化问题的方法,其中优化目标是包含通用控制和数据结构的程序。此类优化问题经常出现在软件工程的日常实践中。虽然标准优化包似乎可以解决这些问题,但事实往往并非如此。这里排除了线性规划等白盒优化方法,因为它们只允许非常有限的目标函数类别。梯度下降和 Nelder-Mead 搜索等黑盒优化技术原则上是适用的,但它们只能在相对平滑的搜索空间中有效,并且由于任意嵌套的分支和循环,即使是简单的程序也可能具有高度不规则、病态的行为。指导该项目的中心思想是来自程序形式推理领域的程序分析技术可以与黑盒优化工具包一起工作,并使得解决更多的问题成为可能。 以上排序比当前可能的排序要多。最终,该项目将产生一个统一的程序优化系统,可以利用优化技术和程序分析策略的灵活组合。由于日常软件开发中面临的许多现实问题都是优化问题,因此该系统将为最终程序员提供一系列新的功能。此外,该研究还将促进通常位于不同学术部门的两个不同研究领域之间的协同作用。
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)
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Armando Solar-Lezama其他文献
Special Issue on Syntax-Guided Synthesis Preface
- DOI:
10.1007/s10703-021-00386-0 - 发表时间:
2022-02-28 - 期刊:
- 影响因子:0.800
- 作者:
Dana Fisman;Rishabh Singh;Armando Solar-Lezama - 通讯作者:
Armando Solar-Lezama
Program sketching
程序草图
- DOI:
10.1007/s10009-012-0249-7 - 发表时间:
2012-08-02 - 期刊:
- 影响因子:1.400
- 作者:
Armando Solar-Lezama - 通讯作者:
Armando Solar-Lezama
SPARLING: Learning Latent Representations with Extremely Sparse Activations
SPARLING:通过极其稀疏的激活学习潜在表示
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Kavi Gupta;Osbert Bastani;Armando Solar-Lezama - 通讯作者:
Armando Solar-Lezama
Metric Program Synthesis
度量程序综合
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
John Feser;Isil Dillig;Armando Solar-Lezama - 通讯作者:
Armando Solar-Lezama
LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions
LEMMA:用学习的符号抽象引导高级数学推理
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Zhening Li;Gabriel Poesia;Omar Costilla-Reyes;Noah Goodman;Armando Solar-Lezama - 通讯作者:
Armando Solar-Lezama
Armando Solar-Lezama的其他文献
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{{ truncateString('Armando Solar-Lezama', 18)}}的其他基金
Expeditions: Collaborative Research: Understanding the World Through Code
探险:合作研究:通过代码了解世界
- 批准号:
1918839 - 财政年份:2020
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
InTrans: TRI-MIT Collaboration on Formal Verification Meets Big Data Intelligence in the Trillion Miles Challenge
InTrans:TRI-MIT 形式验证合作在万亿英里挑战中迎接大数据智能
- 批准号:
1665282 - 财政年份:2017
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
Collaborative Research: Expeditions in Computer Augmented Program Engineering (ExCAPE): Harnessing Synthesis for Software Design
协作研究:计算机增强程序工程探险 (ExCAPE):利用综合进行软件设计
- 批准号:
1139056 - 财政年份:2012
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
SHF: Small: Human-Centered Software Synthesis
SHF:小型:以人为本的软件综合
- 批准号:
1116362 - 财政年份:2011
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
EAGER: Human-Centered Software Synthesis
EAGER:以人为本的软件综合
- 批准号:
1049406 - 财政年份:2010
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
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