Large Language Models (LLMs) have demonstrated remarkable reasoning
abilities, prompting interest in their application as black-box optimizers.
This paper asserts that LLMs possess the capability for zero-shot optimization
across diverse scenarios, including multi-objective and high-dimensional
problems. We introduce a novel population-based method for numerical
optimization using LLMs called Language-Model-Based Evolutionary Optimizer
(LEO). Our hypothesis is supported through numerical examples, spanning
benchmark and industrial engineering problems such as supersonic nozzle shape
optimization, heat transfer, and windfarm layout optimization. We compare our
method to several gradient-based and gradient-free optimization approaches.
While LLMs yield comparable results to state-of-the-art methods, their
imaginative nature and propensity to hallucinate demand careful handling. We
provide practical guidelines for obtaining reliable answers from LLMs and
discuss method limitations and potential research directions.

该研究论文探讨了大型语言模型在零样本优化方面的能力，介绍了一种基于语言模型的进化优化方法（LEO），并通过数值示例验证此假设的有效性。同时，论文还提出使用大型语言模型时需要小心处理其想象性和产生幻觉的特点，并给出了获取可靠答案的实用指南以及讨论了方法局限和潜在的研究方向。

基于大型语言模型的进化优化器：精英主义推理

Large Language Model-Based Evolutionary Optimizer: Reasoning with  elitism

Non-blind deconvolution aims to restore a sharp image from its blurred
counterpart given an obtained kernel. Existing deep neural architectures are
often built based on large datasets of sharp ground truth images and trained
with supervision. Sharp, high quality ground truth images, however, are not
always available, especially for biomedical applications. This severely hampers
the applicability of current approaches in practice. In this paper, we propose
a novel non-blind deconvolution method that leverages the power of deep
learning and classic iterative deconvolution algorithms. Our approach combines
a pre-trained network to extract deep features from the input image with
iterative Richardson-Lucy deconvolution steps. Subsequently, a zero-shot
optimisation process is employed to integrate the deconvolved features,
resulting in a high-quality reconstructed image. By performing the preliminary
reconstruction with the classic iterative deconvolution method, we can
effectively utilise a smaller network to produce the final image, thus
accelerating the reconstruction whilst reducing the demand for valuable
computational resources. Our method demonstrates significant improvements in
various real-world applications non-blind deconvolution tasks.

借助深度学习和经典迭代退卷积算法，本文提出了一种新颖的非盲目退卷积方法，通过预训练网络提取输入图像的深度特征，并结合迭代的 Richardson-Lucy 退卷积步骤，最终通过零样本优化过程获得高质量重建图像，加速重建并减少对计算资源的需求，并在非盲目退卷积任务的各种实际应用中展现了显著的改进。