Extending the reach of automated algorithm design, optimisation and customisation

扩展自动化算法设计、优化和定制的范围

• 批准号: RGPIN-2016-04273
• 负责人: Hoos, Holger
• 金额: $ 4.59万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=614953
• 关键词: Extending; reach; automated; algorithm; design

Challenging computational problems arise prominently in areas such as software verification, energy systems optimisation and analysis of large amounts of data. Efficient software systems for solving these problems are of crucial importance, and improvements to these systems will have considerable economic and societal benefits, e.g., in terms of more sustainable and efficient use of energy and resources. Our research aims at automatically designing, optimising and customising such software for specific application situations. Specifically, our Programming by Optimisation (PbO) approach takes broadly applicable, general-purpose software, makes it flexible and adaptable by encouraging and exposing design choices for key components, and then exploits this flexibility by automatically adapting the software to specific application situations, using advanced machine learning and optimisation techniques. PbO has already attracted much interest in academia and industry; the research proposed here aims to take PbO to the next level, with the goal of establishing this paradigm as a standard way of designing software for computational problems across a wide range of application domains. Towards this end, we will address three major challenges arising in the context of software development using PbO (and beyond). Firstly, it can be very expensive to evaluate configurations or variants of a given piece of software - too expensive to permit direct design optimisation on sets of benchmarks representing the size and difficulty of those problem instances encountered in the intended application. Secondly, e.g., in applications dealing with sensitive data, it may be impossible to perform design optimisation as part of the development process; instead, it may have to be done post-deployment, in the actual application context, using substantially more limited computational resources. Thirdly, creating, managing and assessing design choices can be rather expensive in terms of human expert time. Our methodological work on overcoming these challenges will be guided and validated using three prominent and important applications: - software verification based on state-of-the-art SAT-modulo-theory (SMT) solvers (A1); - automated design and configuration of machine learning pipelines for analysing large amounts of data (A2); and - optimisation of software systems for generation and storage of clean energy (A3). We expect our work, which combines advances in machine learning and optimisation, to take automated algorithm design, optimisation and customisation to the next level, to have transformative impact on the design of software for these and other computationally challenging applications, thus creating very significant value within the information technology sector that produces such software and in the areas that rely on their application.

在软件验证、能源系统优化和大量数据分析等领域，分布式计算问题尤为突出。解决这些问题的有效软件系统至关重要，对这些系统的改进将具有相当大的经济和社会效益，例如，在更可持续和更有效地利用能源和资源方面。我们的研究旨在为特定应用情况自动设计，优化和定制此类软件。 具体来说，我们的优化编程（PbO）方法采用广泛适用的通用软件，通过鼓励和公开关键组件的设计选择使其灵活和适应性强，然后通过使用先进的机器学习和优化技术自动使软件适应特定的应用情况来利用这种灵活性。PbO已经引起了学术界和工业界的极大兴趣;这里提出的研究旨在将PbO提升到一个新的水平，目标是将这种范式建立为一种标准的方法，用于在广泛的应用领域中设计计算问题的软件。 为此，我们将解决在使用PbO（及以后）的软件开发的背景下出现的三个主要挑战。首先，评估给定软件的配置或变体可能非常昂贵-太昂贵而不能允许对代表预期应用中遇到的那些问题实例的大小和难度的基准集进行直接设计优化。其次，例如，在处理敏感数据的应用程序中，可能不可能将设计优化作为开发过程的一部分进行;相反，可能必须在实际应用程序上下文中使用更加有限的计算资源在部署后完成。第三，就人类专家的时间而言，创建、管理和评估设计选择可能相当昂贵。 我们克服这些挑战的方法工作将使用三个突出而重要的应用程序进行指导和验证： - 基于最先进的SAT模理论（SMT）求解器的软件验证（A1）; - 自动设计和配置机器学习管道，用于分析大量数据（A2）;以及 - 优化清洁能源的产生和储存的软件系统（A3）。 我们希望我们的工作结合了机器学习和优化的进步，将自动算法设计，优化和定制提升到一个新的水平，对这些和其他具有计算挑战性的应用程序的软件设计产生变革性影响，从而在生产此类软件的信息技术部门以及依赖其应用的领域创造非常重要的价值。