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Showing 1 to 13 of 13 for “"L-BFGS"”.
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Estratégias de inversão de multiparâmetros utilizando a equação completa da elastodinâmica
… (PolakRibi`ere and Fletcher-Reeves) and the l-BFGS (Quasi-Newton). This analysis reinforces that the l-BFGS method, which makes Hessian approximations iteratively, is the one that better estimates the resulting model, besides converges faster to a satisfactory model. Also, it is evaluated the …
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Traversing Rugged Domains: Explorations in Non-convex Optimization Theory and Software
… combining Particle Swarm Optimization with L-BFGS, and an augmented Lagrangian approach with stochastic inner optimizers that connects constrained optimization with machine learning techniques. Our work combines theoretical foundations with practical implementation, providing researchers tools …
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Techniques for Reliability and Robustness in Integrated Electronic and Photonic Systems
… significantly outperform the gradient-based L-BFGS-B algorithm. New schemes for strategic failure analysis on a subset of the failed units are presented, both for detecting the presence of a second failure mechanism and for improving two-mechanism reliability models. A regression-based protocol …
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Computational optimization of phase change materials on adsorbed carbon dioxide capture systems
… domain isotherms and kinetics. Utilizing the L-BFGS optimization method, a distribution of phase change material within an activated carbon adsorbent bed was determined to improve (CO2) uptake across device designs indicating feasibility for next generation designs.
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Large-Scale Simulations Using First and Second Order Adjoints with Applications in Data Assimilation
… scenarios. The results indicate that the L-BFGS method is the most efficient. Compared with first order adjoints, second order adjoints have not been used to date in air quality simulation. To explore their utility, we show the construction of second order adjoints for chemical transport …
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From Hartree Product to Kohn-Sham and Beyond: Exploring Self-Interaction in Self-Consistent Field Methods
… (TR); to be economical, the solver uses an L-BFGS approximate Hessian and a physically-relevant preconditioner. Coupling these two aspects together is a solver for the TR subproblem that exploits the low-rank structure of the L-BFGS Hessian. We demonstrate that QUOTR is useful, not only for …
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Towards Real-Time Simulation Of Hyperelastic Materials
… our solver can be further accelerated using L-BFGS updates (Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm). Our final method is typically more than ten times faster than one iteration of Newton's method without compromising quality. In fact, our result is often more accurate than …
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Advances in machine learning for sustainable manufacturing
… reduce the training time of the multi-batch L-BFGS to train small convolutional network classifiers implementing a development-based increase of the memory size. Two target manufacturing applications are considered: anomaly detection during silicon wafer etching and material recognition of …
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Optimisation of Phase-Only Computer-Generated Holograms
… Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimisation algorithm together with the Sequential Slicing (SS) technique offers an efficient approach for 3D phase-only CGH by evaluating individual depth slices iteratively as opposed to evaluating the entire volume. The Multi-Frame …
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Inverse Problems in Structural Mechanics
… displacements or strains. The optimizer L-BFGS-B is used to solve the least squares problem. The second problem is the placement optimization of a distributed sensing fiber optic sensor for a smart bed using Genetic Algorithms (GA), where the sensor performance is maximized. The sensing …
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Efficient and Robust ADMM Methods for Dynamics and Geometry Optimization
… ADMM-based algorithm is a combination of an L-BFGS solve in the global step and proximal updates of element stretches in the local step. We also introduce a novel method for dynamic reweighting that is used to adjust element weights at runtime for improved convergence. With both improved …
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Principal Curves in High Dimensions
Principal curves are curves passing through the "center" of a dataset and generalize the notion of principal components to non-linear curves, thus providing more meaningful insights to the structure of the data. SCMS is an elegant and robust method for recovering the principal curves of a dataset. …
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Nanomaterial-Enabled Out-of-Autoclave and Out-of-Oven Manufacturing of Fiber Reinforced Polymer Composites
… with Bound constraints (L-BFGS-B) algorithm was implemented to optimize the cure cycle with respect to time and DoC constraints. Two optimized cure cycles were revealed via the optimization scheme, showing significant (60% to 65%) reductions in manufacturing time. A third …