Pages that link to "Item:Q911463"

The following pages link to On the limited memory BFGS method for large scale optimization (Q911463):

Displaying 50 items.

• \(N\)-double poles solutions for nonlocal Hirota equation with nonzero boundary conditions using Riemann-Hilbert method and PINN algorithm (Q2140112) (← links)
• Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training (Q2145138) (← links)
• A scalable space-time domain decomposition approach for solving large scale nonlinear regularized inverse ill posed problems in 4D variational data assimilation (Q2148116) (← links)
• Limited-memory BFGS with displacement aggregation (Q2149548) (← links)
• A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization (Q2149551) (← links)
• Particle gradient descent model for point process generation (Q2152556) (← links)
• Information geometry of physics-informed statistical manifolds and its use in data assimilation (Q2162022) (← links)
• Parameters estimation in Ebola virus transmission dynamics model based on machine learning (Q2164308) (← links)
• Nonmonotone diagonally scaled limited-memory BFGS methods with application to compressive sensing based on a penalty model (Q2165892) (← links)
• Fractional Chebyshev deep neural network (FCDNN) for solving differential models (Q2169390) (← links)
• Optimal reduced space for variational data assimilation (Q2169496) (← links)
• Nonmonotone spectral gradient method based on memoryless symmetric rank-one update for large-scale unconstrained optimization (Q2171075) (← links)
• Modified projection methods for solving multi-valued variational inequality without monotonicity (Q2172748) (← links)
• A hybrid quasi-Newton projected-gradient method with application to lasso and basis-pursuit denoising (Q2175442) (← links)
• Limited memory BFGS algorithm for the matrix approximation problem in Frobenius norm (Q2176186) (← links)
• Designing a stable feedback control system for blind image deconvolution (Q2179814) (← links)
• Packing rectangles into a fixed size circular container: constructive and metaheuristic search approaches (Q2184044) (← links)
• Diagonal approximation of the Hessian by finite differences for unconstrained optimization (Q2188948) (← links)
• New conjugate gradient algorithms based on self-scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method (Q2190791) (← links)
• A double parameter self-scaling memoryless BFGS method for unconstrained optimization (Q2190850) (← links)
• On the extension of the Hager-Zhang conjugate gradient method for vector optimization (Q2191796) (← links)
• Open-loop optimal control of a flapping wing using an adjoint lattice Boltzmann method (Q2192531) (← links)
• Bayesian variable selection for survival data using inverse moment priors (Q2194467) (← links)
• Correctness of automatic differentiation via diffeologies and categorical gluing (Q2200831) (← links)
• Lower bounds for finding stationary points I (Q2205972) (← links)
• Use of projective coordinate descent in the Fekete problem (Q2207557) (← links)
• Data-driven discovery of PDEs in complex datasets (Q2214651) (← links)
• Optimization techniques for tree-structured nonlinear problems (Q2221477) (← links)
• Adversarial uncertainty quantification in physics-informed neural networks (Q2222278) (← links)
• Transition pathways between defect patterns in confined nematic liquid crystals (Q2222398) (← links)
• Global optimization for data assimilation in landslide tsunami models (Q2222990) (← links)
• Coercing machine learning to output physically accurate results (Q2223280) (← links)
• Minimizing a sum of clipped convex functions (Q2228412) (← links)
• Estimation of spatially varying open boundary conditions for a numerical internal tidal model with adjoint method (Q2229797) (← links)
• Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (Q2233120) (← links)
• Physics-informed multi-LSTM networks for metamodeling of nonlinear structures (Q2236167) (← links)
• Hidden physics model for parameter estimation of elastic wave equations (Q2236961) (← links)
• Solving inverse problems in stochastic models using deep neural networks and adversarial training (Q2237477) (← links)
• Physics informed by deep learning: numerical solutions of modified Korteweg-de Vries equation (Q2244291) (← links)
• Pseudo-feasible solutions in evolutionary bilevel optimization: test problems and performance assessment (Q2246047) (← links)
• Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
• European spreads at the interest rate lower bound (Q2246719) (← links)
• Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (Q2246919) (← links)
• Damped techniques for the limited memory BFGS method for large-scale optimization (Q2247912) (← links)
• A modified ODE-based algorithm for unconstrained optimization problems (Q2248961) (← links)
• A regularized limited memory BFGS method for nonconvex unconstrained minimization (Q2248965) (← links)
• A practical PR+ conjugate gradient method only using gradient (Q2250230) (← links)
• Sequence labeling with multiple annotators (Q2251473) (← links)
• A nonmonotone trust region method based on simple quadratic models (Q2253065) (← links)
• Accelerated scaled memoryless BFGS preconditioned conjugate gradient algorithm for unconstrained optimization (Q2267641) (← links)