Tootfinder

Optimal Box Contraction for Solving Linear Systems via Simulated and Quantum Annealing
Sanjay Suresh, Krishnan Suresh
https://arxiv.org/abs/2405.03029 http…

Optimal Box Contraction for Solving Linear Systems via Simulated and Quantum Annealing
Solving linear systems of equations is an important problem in science and engineering. Many quantum algorithms, such as the Harrow-Hassidim-Lloyd (HHL) algorithm (for quantum-gate computers) and the box algorithm (for quantum-annealing machines), have been proposed for solving such systems. The focus of this paper is on improving the efficiency of the box algorithm. The basic principle behind this algorithm is to transform the linear system into a series of quadratic unconstrained binary opt…

Assigning Entities to Teams as a Hypergraph Discovery Problem
Guilherme Ferraz de Arruda, Wan He, Nasimeh Heydaribeni, Tara Javidi, Yamir Moreno, Tina Eliassi-Rad
https://arxiv.org/abs/2403.04063

Assigning Entities to Teams as a Hypergraph Discovery Problem
We propose a team assignment algorithm based on a hypergraph approach focusing on resilience and diffusion optimization. Specifically, our method is based on optimizing the algebraic connectivity of the Laplacian matrix of an edge-dependent vertex-weighted hypergraph. We used constrained simulated annealing, where we constrained the effort agents can exert to perform a task and the minimum effort a task requires to be completed. We evaluated our methods in terms of the number of unsuccessful pa…

Constructing Bayesian Optimal Designs for Discrete Choice Experiments by Simulated Annealing
Yicheng Mao, Roselinde Kessels, Tom van der Zanden
https://arxiv.org/abs/2402.18533

Constructing Bayesian Optimal Designs for Discrete Choice Experiments by Simulated Annealing
Discrete Choice Experiments (DCEs) investigate the attributes that influence individuals' choices when selecting among various options. To enhance the quality of the estimated choice models, researchers opt for Bayesian optimal designs that utilize existing information about the attributes' preferences. Given the nonlinear nature of choice models, the construction of an appropriate design requires efficient algorithms. Among these, the Coordinate-Exchange (CE) algorithm is most commonly employe…

Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy
Zixi Wang, Yubo Huang, Changshuo Fan, Xin Lai, Peng Lu
https://arxiv.org/abs/2403.19484

Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy
This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare settings. Medical robotics, particularly in vascular treatments, necessitates precise resource allocation and optimization due to the complex nature of robot and operator maintenance. Traditional heuristic methods, though intuitive, often fail to achieve global optimization. To address these challenges, this research introduces a novel strategy, combin…

Optimal schedules for annealing algorithms
Amin Barzegar, Firas Hamze, Christopher Amey, Jonathan Machta
https://arxiv.org/abs/2402.14717 https://

Optimal schedules for annealing algorithms
Annealing algorithms such as simulated annealing and population annealing are widely used both for sampling the Gibbs distribution and solving optimization problems (i.e. finding ground states). For both statistical mechanics and optimization, additional parameters beyond temperature are often needed such as chemical potentials, external fields or Lagrange multipliers enforcing constraints. In this paper we derive a formalism for optimal annealing schedules in multidimensional parameter spaces …

This super sounds like re-introducing symbolic #AI using machine learning systems to build options to run goal-oriented optimization functions on. Like I wonder how OpenAI et al will re-brand all the old methods like simulated annealing etc. and claim to have invented the future.

This https://arxiv.org/abs/2309.02735 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Fast Simulated Annealing inspired by Quantum Monte Carlo
Quantum Monte Carlo (QMC) is commonly used in simulations for Quantum Annealing (QA), but QMC as a heuristic approach has great difficulty in that it takes much time to find minimum energy. It mainly depends on the existence of a trotter layer derived from Suzuki-Trotter decomposition. In this paper, I propose a new approach to calculate it in short time, although it isn't rigorous mathematically. Its validity and advantageous points are also discussed, in comparison with conventional QMC metho…

This https://arxiv.org/abs/2401.10519 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

A Wind-Aware Path Planning Method for UAV-Asisted Bridge Inspection
In response to the gap in considering wind conditions in the bridge inspection using unmanned aerial vehicle (UAV) , this paper proposes a path planning method for UAVs that takes into account the influence of wind, based on the simulated annealing algorithm. The algorithm considers the wind factors, including the influence of different wind speeds and directions at the same time on the path planning of the UAV. Firstly, An environment model is constructed specifically for UAV bridge inspection…

Double-$Q$ and quadruple-$Q$ instabilities at low-symmetric ordering wave vectors under tetragonal symmetry
Satoru Hayami
https://arxiv.org/abs/2402.11721 …

Double-$Q$ and quadruple-$Q$ instabilities at low-symmetric ordering wave vectors under tetragonal symmetry
Multiple-$Q$ states are expressed as a superposition of spin density waves at multiple ordering wave vectors, which results in unconventional complicated spin textures, such as skyrmion, hedgehog, and vortex. We investigate the multiple-$Q$ instability by focusing on the low-symmetric ordering wave vectors in momentum space. By systematically performing the simulated annealing for effective spin models with various ordering wave vectors on a two-dimensional square lattice, we classify the magne…