arXiv:2608.21187v1 Announce Type: cross
Abstract: Pairwise reciprocal matrices are fundamental to the Analytic Hierarchy Process (AHP), a decision-making model. While the Direct Least Squares (DLS) m...
By Kevin Kam Fung Yuen
arXiv:2609.14307v1 Announce Type: new
Abstract: Low-rank tensor factorization provides a flexible framework for completing multidimensional data from incomplete and corrupted observations. However, u...
By Binghao Wang, Feng Zhang, Wendong Wang, Jianjun Wang
arXiv:2609.37308v1 Announce Type: cross
Abstract: This paper introduces a hybrid joint-selective optimization (HJSO) framework for large-scale numerical problems in which a small subset of trainable...
By Muhammad Luthfi Shahab, Gabriella Alfa Indahsari, Imam Mukhlash, Hadi Susanto
arXiv:2607. 10038v1 Announce Type: cross Abstract: For many years, the pairwise comparison method has been widely used for decision-making involving experts.
By Konrad Ku{\l}akowski, Jacek Szybowski
arXiv:2609.26077v1 Announce Type: new
Abstract: A Strassen-type algorithm has many realizations with the same exact product and multiplication count yet different fp8 error because basis changes resh...
By Shuxiao Xie, Shuyang Xie, Yuan Cao, Dezhi Ran, Wei Yang, Tao Xie
The paper introduces DA‑EGO, an efficient global optimization algorithm that dynamically aggregates high‑dimensional design spaces into low‑dimensional subspaces for surrogate‑based search. The algorithm updates subspace variables each iteration using variable‑interaction analyses, perturbation, and ANOVA, and adaptively adjusts search ranges based on previous results. Tests on 21 benchmark functions and real turbomachinery problems demonstrate DA‑EGO’s effectiveness, especially on separable and partially separable problems, while noting case‑dependent performance on non‑separable functions.
By Qineng Wang, Zhendong Guo, Yun Chen, Guangjian Ma, Liming Song, Jun Li