Results 111 to 120 of about 108,734 (233)

Efficient Gradient‐Enhanced Bayesian Optimizer with Comparisons to Conjugate‐Gradient and Quasi‐Newton Optimizers for Unconstrained Local Optimization

International Journal for Numerical Methods in Engineering, Volume 127, Issue 4, 28 February 2026.
ABSTRACT The probabilistic surrogates used by Bayesian optimizers make them popular methods when function evaluations are noisy or expensive to evaluate. While Bayesian optimizers are traditionally used for global optimization, their benefits are also valuable for local optimization.
André L. Marchildon, David W. Zingg
wiley   +1 more source

Reliable AI Platform for Monitoring BCI Caused Brain Injury and Providing Real‐Time Protection

Advanced Science, Volume 13, Issue 7, 3 February 2026.
BrainGuard enables real‐time and interpretable assessment of brain injury caused by brain computer interface (BCI). Using feature‐based Gaussian process (GP) emulators trained on limited biomechanical data, it efficiently predicts full‐field strain and constructs patient‐specific digital brain twins to support clinical diagnosis and long‐term BCI ...
Chufan He, Yanjun Ding, Timon Rabczuk, Chensen Ding   +3 more
wiley   +1 more source

Improved New Two-Spectral Conjugate Gradient Iterative Technique for Large Scale Optimization

Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ
Numerous strategies have been proposed in the field of unconstrained optimization to address various optimization challenges, particularly those associated with large-scale systems. Among the classical methods, Newton and Quasi-Newton approaches are well-
Radhwan Basem Thanoon, Ghada Moayid Al-Naemi   +1 more
doaj   +1 more source

Mapping Quantum Computing Techniques for NP‐Hard Problems in Operations Management and Operations Research

Engineering Reports, Volume 8, Issue 2, February 2026.
Quantum computing techniques such as Quantum Annealing and Quadratic Unconstrained Binary Optimization are effectively solving NP‐hard problems in operations management and research, particularly in logistics, manufacturing, and finance. This study maps these applications to present a framework for future adoption across industries. ABSTRACT This study
Daniel Bouzon Nagem Assad, Patricia Gomes Ferreira da Costa, Thaís Spiegel, Hélcio de Oliveira Rocha   +3 more
wiley   +1 more source

Characterization of Multimodality in Wind Farm Layout Optimization

Energy Science &Engineering, Volume 14, Issue 2, Page 737-751, February 2026.
ABSTRACT The phenomena of multiple optima in wind farm layout optimization (WFLO) problems is investigated. The choice of optimization algorithm and cost of solving WFLO problems is driven by the degree of local optimality in the design space; however little work has attempted to characterize this.
Daniel J. Poole
wiley   +1 more source

A Polynomial-Time Algorithm for Unconstrained Binary Quadratic Optimization

, 2020
In this paper, an exact algorithm in polynomial time is developed to solve unrestricted binary quadratic programs. The computational complexity is $O\left( n^{\frac{15}{2}}\right) $, although very conservative, it is sufficient to prove that this minimization problem is in the complexity class $P$.
openaire   +2 more sources

Quantum annealing and its variants: application to quadratic unconstrained binary optimization

, 2023
Published by RWTH Aachen University ...
openaire   +3 more sources

A Quadratic Unconstrained Binary Optimization (QUBO) Model for Elevation‐Aware Vehicle Routing: Optimizing Fuel Consumption and Traffic Congestion

Advanced Quantum Technologies, Volume 9, Issue 2, February 2026.
This study proposes an elevation‐aware QUBO model that optimizes multi‐vehicle routes by jointly reducing fuel consumption and traffic congestion. By integrating gradient‐corrected Dijkstra routing with quantum annealing, the method achieves substantial fuel‐saving effects in hilly regions such as San Francisco and significantly decreases route overlap,
Tsubasa Suzuki, Takao Tomono
wiley   +1 more source

Stabilizing Inference in Dirichlet Regression via Ridge‐Penalized Model

Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.
ABSTRACT We propose a penalized Dirichlet regression framework for modeling compositional data, using a softmax link to ensure that the mean vector lies on the simplex and to avoid log‐ratio transformations or zero replacement. The model is formulated in a GLM‐like setting and incorporates an ℓ2$$ {\mathrm{\ell}}_2 $$ (ridge) penalty on the regression ...
Andrea Nigri
wiley   +1 more source

Predictor/estimator computations under a constrained multivariate linear model and some related reduced models

Miskolc Mathematical Notes
This study deals with basic prediction/estimation issues involving a constrained multivariate linear model (CMLM) and some related reduced models. By reparameterizing these models, the authors create unconstrained multivariate linear models (UMLMs ...
Melek Eriş Büyükkaya, Nesrin Güler   +1 more
doaj   +1 more source