Minimum connected dominating set problem is an NP-hard combinatorial
optimization problem in graph theory. Finding connected dominating set is of
high interest in various domains such as wireless sensor networks, optical
networks, and systems biology. Its weighted variant named minimum weight
connected dominating set is also useful in such applications. In this paper, we
propose a simulated annealing algorithm based on a greedy heuristic for
tackling a variant of the minimum connected dominating set problem and that by
exploiting two objectives together namely the cardinality and the total weight
of the connected dominating set. Experimental results compared to those
obtained by a recent proposed research show the superiority of our approach.

本文提出基于贪心启发式的模拟退火算法，用于解决最小连通支配集问题的一个变体，通过同时利用支配集的基数和总权重两个目标来进行优化，实验结果表明我们的方法优于最近提出的研究。

一种基于模拟退火的多目标优化算法求解最小重量最小连接主导集问题

A Simulated Annealing-Based Multiobjective Optimization Algorithm for  Minimum Weight Minimum Connected Dominating Set Problem

Recent improvements in text generation have leveraged human feedback to
improve the quality of the generated output. However, human feedback is not
always available, especially during inference. In this work, we propose an
inference time optimization method FITO to use fine-grained actionable feedback
in the form of error type, error location and severity level that are predicted
by a learned error pinpoint model for iterative refinement. FITO starts with an
initial output, then iteratively incorporates the feedback via a refinement
model that generates an improved output conditioned on the feedback. Given the
uncertainty of consistent refined samples at iterative steps, we formulate
iterative refinement into a local search problem and develop a simulated
annealing based algorithm that balances exploration of the search space and
optimization for output quality. We conduct experiments on three text
generation tasks, including machine translation, long-form question answering
(QA) and topical summarization. We observe 0.8 and 0.7 MetricX gain on
Chinese-English and English-German translation, 4.5 and 1.8 ROUGE-L gain at
long form QA and topic summarization respectively, with a single iteration of
refinement. With our simulated annealing algorithm, we see further quality
improvements, including up to 1.7 MetricX improvements over the baseline
approach.

使用细粒度的可行操作反馈，基于学习的错误定位模型预测的错误类型、错误位置和严重程度，提出了 FITO（一种推理时间优化方法）来进行迭代改进，通过一个生成改进输出的改进模型，迭代地结合反馈。我们在三个文本生成任务上进行了实验，包括机器翻译、长篇问答（QA）和主题摘要，在单次迭代的改进中，中英翻译和英德翻译分别观察到 0.8 和 0.7 的 MetricX 增益，问答和主题摘要分别观察到 4.5 和 1.8 的 ROUGE-L 增益。通过我们的模拟退火算法，我们看到进一步的质量改进，包括与基准方法相比高达 1.7 的 MetricX 改进。

精准指点，而非批评：通过精细的可操作反馈改进大型语言模型

Pinpoint, Not Criticize: Refining Large Language Models via Fine-Grained  Actionable Feedback

We outline an unsupervised method for temporal rank ordering of sets of
historical documents, namely American State of the Union Addresses and DEEDS, a
corpus of medieval English property transfer documents. Our method relies upon
effectively capturing the gradual change in word usage via a bandwidth estimate
for the non-parametric Generalized Linear Models (Fan, Heckman, and Wand,
1995). The number of possible rank orders needed to search through possible
cost functions related to the bandwidth can be quite large, even for a small
set of documents. We tackle this problem of combinatorial optimization using
the Simulated Annealing algorithm, which allows us to obtain the optimal
document temporal orders. Our rank ordering method significantly improved the
temporal sequencing of both corpora compared to a randomly sequenced baseline.
This unsupervised approach should enable the temporal ordering of undated
document sets.

我们概述了一种针对历史文件集的无监督方法，即《美国国情咨文演讲》和 DEEDS，这是一本中世纪英国土地转让文件的语料库。我们的方法通过非参数广义线性模型（Fan、Heckman 和 Wand，1995）的带宽估计有效捕捉词语使用的渐变变化。即使对于少量文档，所需搜索与带宽相关的可能成本函数的排名顺序数量可能非常大。我们使用模拟退火算法解决了这个组合优化问题，从而使我们能够获得最佳的文档时间顺序。与随机排序的基线相比，我们的排名方法显著改善了这两个语料库的时间排序。这种无监督方法应能够对未标记的文档集进行时间排序。