Results 71 to 80 of about 43,906 (221)

Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions

, 2020
This study investigates the optimization aspects of imposing hard inequality constraints on the outputs of CNNs. In the context of deep networks, constraints are commonly handled with penalties for their simplicity, and despite their well-known ...
Ayed, Ismail Ben, Desrosiers, Christian, Dolz, Jose, Granger, Eric, Kervadec, Hoel, Yuan, Jing   +5 more
core  

A Fast First-Order Optimization Approach to Elastoplastic Analysis of Skeletal Structures

, 2016
It is classical that, when the small deformation is assumed, the incremental analysis problem of an elastoplastic structure with a piecewise-linear yield condition and a linear strain hardening model can be formulated as a convex quadratic programming ...
Kanno, Yoshihiro
core   +1 more source

Solving (Max) 3-SAT via Quadratic Unconstrained Binary Optimization

, 2023
We introduce a novel approach to translate arbitrary 3-SAT instances to Quadratic Unconstrained Binary Optimization (QUBO) as they are used by quantum annealing (QA) or the quantum approximate optimization algorithm (QAOA). Our approach requires fewer couplings and fewer physical qubits than the current state-of-the-art, which results in higher ...
Jonas Nüßlein, Sebastian Zielinski, Thomas Gabor, Claudia Linnhoff-Popien, Sebastian Feld   +4 more
openaire   +2 more sources

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini, Shaghayegh Karimzadeh, Paulo B. Lourenço   +2 more
wiley   +1 more source

Prime factorization using quantum annealing and computational algebraic geometry

, 2016
We investigate prime factorization from two perspectives: quantum annealing and computational algebraic geometry, specifically Gr\"obner bases. We present a novel scalable algorithm which combines the two approaches and leads to the factorization of all ...
Alghassi, Hedayat, Dridi, Raouf
core   +1 more source

Hybrid Osprey‐Salp Swarm Optimization Algorithm for Single and Multiobjective Optimal Power Flow in Smart Grids With Renewable Energy Integration

Energy Science &Engineering, EarlyView.
The proposed hybrid osprey‐salp swarm optimization algorithm addresses optimal power flow (OPF) problems in smart grids incorporating solar, hydro, and thermal generators. The algorithm is validated on Institute of Electrical and Electronics Engineers 30‐, 57‐, and 118‐bus test systems across five single and multiobjective OPF scenarios.
Mujtaba Ali, Muhammad Yaqoob Javed, Aamer Bilal Asghar, Gang Xiong, Krzysztof Ejsmont, Xiaoli Liu   +5 more
wiley   +1 more source

Analytical Approximation-Based Approach for Passenger Flow Control Strategy in Oversaturated Urban Rail Transit Systems

Journal of Advanced Transportation, 2023
Focusing on a heavily congested urban rail corridor, this study investigates the passenger flow control strategy optimization problem from a mesoscopic perspective to reduce platform congestion and enhance service quality. Based on a quadratic functional
Qian Zhu, Xiaoning Zhu, Pan Shang, Lingyun Meng   +3 more
doaj   +1 more source

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

An Efficient Subspace Minimization Conjugate Gradient Method for Solving Nonlinear Monotone Equations with Convex Constraints

Axioms
The subspace minimization conjugate gradient (SMCG) methods proposed by Yuan and Store are efficient iterative methods for unconstrained optimization, where the search directions are generated by minimizing the quadratic approximate models of the ...
Taiyong Song, Zexian Liu
doaj   +1 more source

QUBO.jl: A Julia Ecosystem for Quadratic Unconstrained Binary Optimization

, 2023
We present QUBO.jl, an end-to-end Julia package for working with QUBO (Quadratic Unconstrained Binary Optimization) instances. This tool aims to convert a broad range of JuMP problems for straightforward application in many physics and physics-inspired solution methods whose standard optimization form is equivalent to the QUBO.
Xavier, Pedro Maciel, Ripper, Pedro, Andrade, Tiago, Garcia, Joaquim Dias, Maculan, Nelson, Neira, David E. Bernal   +5 more
openaire   +2 more sources