Extending the reach of automated algorithm design, optimisation and customisation

具有挑战性的计算问题在软件验证、能源系统优化和大量数据分析等领域突出出现。解决这些问题的有效软件系统是至关重要的，对这些系统的改进将有相当大的经济和社会效益，例如，在更可持续和有效地利用能源和资源方面。我们的研究旨在针对特定的应用情况自动设计、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，通过鼓励和暴露关键组件的设计选择，使其灵活和适应性强，然后利用这种灵活性，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。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，其目标是将这种范式建立为跨广泛应用领域的计算问题设计软件的标准方法。为此，我们将解决在使用PbO（及其他）的软件开发环境中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——太昂贵了，无法在代表预期应用程序中遇到的那些问题实例的大小和难度的基准集上进行直接设计优化。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；相反，它可能必须在部署后，在实际的应用程序上下文中使用更有限的计算资源来完成。第三，创造、管理和评估设计选择在人力专家时间方面是相当昂贵的。***我们克服这些挑战的方法工作将通过三个突出和重要的应用来指导和验证：***-基于最先进的sat模块理论（SMT）求解器（A1）的软件验证；***-用于分析大量数据的机器学习管道的自动化设计和配置（A2）；***-清洁能源发电和存储软件系统的优化（A3）。***我们期望我们的工作，结合机器学习和优化的进步，将自动化算法设计，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的影响，从而在生产此类软件的信息技术部门和依赖于其应用的领域创造非常重要的价值

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.**