Tootfinder

An Analytical Framework for the Linear Best-Worst Method and its Application to Achieve Sustainable Development Goals--Oriented Agri-Food Supply Chains
Harshit M. Ratandhara, Mohit Kumar
https://arxiv.org/abs/2506.17666

An Analytical Framework for the Linear Best-Worst Method and its Application to Achieve Sustainable Development Goals--Oriented Agri-Food Supply Chains
The Best-Worst Method (BWM) has emerged as a prominent multi-criteria decision-making method for determining the weights of the decision criteria. Among various BWM models, this research focuses on the linear model of the BWM. This model calculates weights by solving an optimization problem, necessitating optimization software. In this article, we present a novel framework that solves this optimization model mathematically, yielding an analytical expression for the resultant weights, thus elimi…

Robust Optimal Task Planning to Maximize Battery Life
Jiachen Li, Chu Jian, Feiyang Zhao, Shihao Li, Wei Li, Dongmei Chen
https://arxiv.org/abs/2506.11264 …

Robust Optimal Task Planning to Maximize Battery Life
This paper proposes a control-oriented optimization platform for autonomous mobile robots (AMRs), focusing on extending battery life while ensuring task completion. The requirement of fast AMR task planning while maintaining minimum battery state of charge, thus maximizing the battery life, renders a bilinear optimization problem. McCormick envelop technique is proposed to linearize the bilinear term. A novel planning algorithm with relaxed constraints is also developed to handle parameter unce…

A Note on the Reliability of Goal-Oriented Error Estimates for Galerkin Finite Element Methods with Nonlinear Functionals
Brian N. Granzow, Stephen D. Bond, D. Thomas Seidl, Bernhard Endtmayer
https://arxiv.org/abs/2506.09913

A Note on the Reliability of Goal-Oriented Error Estimates for Galerkin Finite Element Methods with Nonlinear Functionals
We consider estimating the discretization error in a nonlinear functional $J(u)$ in the setting of an abstract variational problem: find $u \in \mathcal{V}$ such that $B(u,φ) = L(φ) \; \forall φ\in \mathcal{V}$, as approximated by a Galerkin finite element method. Here, $\mathcal{V}$ is a Hilbert space, $B(\cdot,\cdot)$ is a bilinear form, and $L(\cdot)$ is a linear functional. We consider well-known error estimates $η$ of the form $J(u) - J(u_h) \approx η= L(z) - B(u_h, z)$, where $u_h$ d…

This https://arxiv.org/abs/2501.15056 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Feedback-Aware Monte Carlo Tree Search for Efficient Information Seeking in Goal-Oriented Conversations
Effective decision-making and problem-solving in conversational systems require the ability to identify and acquire missing information through targeted questioning. A key challenge lies in efficiently narrowing down a large space of possible outcomes by posing questions that minimize uncertainty. To address this, we introduce a novel framework that leverages Large Language Models (LLMs) to generate information-seeking questions, with Monte Carlo Tree Search (MCTS) to strategically select quest…

Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
https://arxiv.org/abs/2506.04952

Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
The problem of resource allocation in goal-oriented semantic communication with semantic-aware utilities and subjective risk perception is studied here. By linking information importance to risk aversion, we model agent behavior using Cumulative Prospect Theory (CPT), which incorporates risk-sensitive utility functions and nonlinear transformations of distributions, reflecting subjective perceptions of gains and losses. The objective is to maximize the aggregate utility across multiple CPT-mode…

This https://arxiv.org/abs/2505.19189 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval
Although Multi-Vector Retrieval (MVR) has achieved the state of the art on many information retrieval (IR) tasks, its performance highly depends on how to decompose queries into smaller pieces, say phrases or tokens. However, optimizing query decomposition for MVR performance is not end-to-end differentiable. Even worse, jointly solving this problem and training the downstream retrieval-based systems, say RAG systems could be highly inefficient. To overcome these challenges, we propose Performa…

An emergence-oriented approach to cyclic pursuit
Zhaozhan Yao, Yuhua Yao, Xiaoming Hu
https://arxiv.org/abs/2506.05157 https://arxiv.…

An emergence-oriented approach to cyclic pursuit
In this paper, we explore the cyclic pursuit problem of unicycles and study circular formation from an emergence perspective. We first establish a systematic study on such formation and derive a necessary and sufficient condition for its existence. Building on this theoretical foundation, we design a distributed control law that enables the spontaneous formation of circular formations through only local interactions. Notably, key geometric features -- including the radius and agent spacing -- a…