大语言模型视角下的鲁棒与自适应优化
Robust and Adaptive Optimization under a Large Language Model Lens
AI总结：
本文研究利用ChatGPT结合专门提示自动构建、鲁棒化并求解简单鲁棒与自适应优化问题，展示其在专业小众领域通过上下文学习提升表现的潜力。
摘要：
本文探讨了ChatGPT在鲁棒优化与自适应鲁棒优化领域的应用。我们证明，通过适当的提示（prompting），ChatGPT可用于自动构建并求解简单的鲁棒优化和自适应优化问题。我们首先针对自适应优化和鲁棒优化领域开发了专门的信息性提示。随后，我们展示利用这些提示，ChatGPT能够（i）构建一个优化问题，（ii）调整该问题以纳入用户指定的不确定性，（iii）推导出该问题在计算上可处理的鲁棒对等问题（robust counterpart），以及（iv）生成求解该问题的可执行代码。对于简单的优化问题，我们表明ChatGPT能够以极少甚至零错误完成这些步骤。我们还着重指出了一些步骤执行错误的实例。总体而言，我们证明通过上下文学习（in-context learning），ChatGPT有潜力适应高度专业化和细分的小众领域，而在这些领域中，其开箱即用的表现原本会非常差。
英文摘要：
In this paper, we explore the application of ChatGPT in the domain of Robust and Adaptive Robust Optimization. We demonstrate that with appropriate prompting, ChatGPT can be used to auto-formulate and solve simple Robust and Adaptive Optimization Problems. We first develop specialized informational prompts tailored to the domains of Adaptive and Robust Optimization. Then, we show that using these prompts, ChatGPT is able to (i) formulate an optimization problem, (ii) adapt the problem so that it accounts for user-specified uncertainty, (iii) derive the computationally tractable robust counterpart of the problem and (iv) produce executable code that solves the problem. For simple Optimization Problems, we show that ChatGPT is able to perform these steps with little to no errors. We also highlight some instances of erroneous execution of the steps. Overall, we show that using in-context learning, ChatGPT has the potential to adapt to higlhy specialized and niche domains, in which it would otherwise demonstrate very poor out-of-the-box performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
