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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "GRADIENT COMPUTATION"

1

Ding, Zhiyan, and Qin Li. "Constrained Ensemble Langevin Monte Carlo." Foundations of Data Science 4, no. 1 (2022): 37. http://dx.doi.org/10.3934/fods.2021034.

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<p style='text-indent:20px;'>The classical Langevin Monte Carlo method looks for samples from a target distribution by descending the samples along the gradient of the target distribution. The method enjoys a fast convergence rate. However, the numerical cost is sometimes high because each iteration requires the computation of a gradient. One approach to eliminate the gradient computation is to employ the concept of "ensemble." A large number of particles are evolved together so the neighboring particles provide gradient information to each other. In this article, we discuss two algorithms that integrate the ensemble feature into LMC, and the associated properties.</p><p style='text-indent:20px;'>In particular, we find that if one directly surrogates the gradient using the ensemble approximation, the algorithm, termed Ensemble Langevin Monte Carlo, is unstable due to a high variance term. If the gradients are replaced by the ensemble approximations only in a constrained manner, to protect from the unstable points, the algorithm, termed Constrained Ensemble Langevin Monte Carlo, resembles the classical LMC up to an ensemble error but removes most of the gradient computation.</p>
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2

B N, Shobha, Govind R. Kadambi, S. R. Shankapal, and Yuri Vershinim. "Effect of variation in colour gradient information for optic flow computations." International Journal of Engineering & Technology 3, no. 4 (September 17, 2014): 445. http://dx.doi.org/10.14419/ijet.v3i4.2722.

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Optic flow algorithms provide mapping of 3D velocities on 2D image space. Optic flow is computed on the pair of images which are in sequence and is normally gray scale images. Optic flow computation using Horn and Schunck assumes that brightness consistency is maintained. Colour optic flow has the advantage that optic flow vectors are obtained even when there is a variation of brightness in the input images. The use of colour bands for optic flow is investigated by considering gradients of colour bands and component gradients. Results of applying these two types of gradients to three colour models are presented and analyzed. Decision logic is proposed to select the best colour model for colour optic flow computation based on gradient analysis. Keywords: Activity Measure. Colour Bands, Component Gradients, Decision Logic, Optic Flow Computation.
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3

Sengupta, B., K. J. Friston, and W. D. Penny. "Efficient gradient computation for dynamical models." NeuroImage 98 (September 2014): 521–27. http://dx.doi.org/10.1016/j.neuroimage.2014.04.040.

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4

Xu, Jingyan, and Frederic Noo. "Efficient gradient computation for optimization of hyperparameters." Physics in Medicine & Biology 67, no. 3 (February 7, 2022): 03NT01. http://dx.doi.org/10.1088/1361-6560/ac4442.

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Abstract We are interested in learning the hyperparameters in a convex objective function in a supervised setting. The complex relationship between the input data to the convex problem and the desirable hyperparameters can be modeled by a neural network; the hyperparameters and the data then drive the convex minimization problem, whose solution is then compared to training labels. In our previous work (Xu and Noo 2021 Phys. Med. Biol. 66 19NT01), we evaluated a prototype of this learning strategy in an optimization-based sinogram smoothing plus FBP reconstruction framework. A question arising in this setting is how to efficiently compute (backpropagate) the gradient from the solution of the optimization problem, to the hyperparameters to enable end-to-end training. In this work, we first develop general formulas for gradient backpropagation for a subset of convex problems, namely the proximal mapping. To illustrate the value of the general formulas and to demonstrate how to use them, we consider the specific instance of 1D quadratic smoothing (denoising) whose solution admits a dynamic programming (DP) algorithm. The general formulas lead to another DP algorithm for exact computation of the gradient of the hyperparameters. Our numerical studies demonstrate a 55%–65% computation time savings by providing a custom gradient instead of relying on automatic differentiation in deep learning libraries. While our discussion focuses on 1D quadratic smoothing, our initial results (not presented) support the statement that the general formulas and the computational strategy apply equally well to TV or Huber smoothing problems on simple graphs whose solutions can be computed exactly via DP.
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5

Hill, S. "Reduced gradient computation in prediction error identification." IEEE Transactions on Automatic Control 30, no. 8 (August 1985): 776–78. http://dx.doi.org/10.1109/tac.1985.1104062.

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6

Calugaru, Dan-Gabriel, and Jean-Marie Crolet. "Gradient computation in a nonlinear inverse problem." Comptes Rendus Mathematique 336, no. 8 (April 2003): 691–96. http://dx.doi.org/10.1016/s1631-073x(03)00130-4.

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7

Berlin, Konstantin, Nail A. Gumerov, David Fushman, and Ramani Duraiswami. "HierarchicalO(N) computation of small-angle scattering profiles and their associated derivatives." Journal of Applied Crystallography 47, no. 2 (March 28, 2014): 755–61. http://dx.doi.org/10.1107/s1600576714004671.

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The need for fast approximate algorithms for Debye summation arises in computations performed in crystallography, small/wide-angle X-ray scattering and small-angle neutron scattering. When integrated into structure refinement protocols these algorithms can provide significant speed up over direct all-atom-to-all-atom computation. However, these protocols often employ an iterative gradient-based optimization procedure, which then requires derivatives of the profile with respect to atomic coordinates. This article presents an accurate,O(N) cost algorithm for the computation of scattering profile derivatives. The results reported here show orders of magnitude improvement in computational efficiency, while maintaining the prescribed accuracy. This opens the possibility to efficiently integrate small-angle scattering data into the structure determination and refinement of macromolecular systems.
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8

Zhang, Jianfei, and Lei Zhang. "Efficient CUDA Polynomial Preconditioned Conjugate Gradient Solver for Finite Element Computation of Elasticity Problems." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/398438.

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Graphics processing unit (GPU) has obtained great success in scientific computations for its tremendous computational horsepower and very high memory bandwidth. This paper discusses the efficient way to implement polynomial preconditioned conjugate gradient solver for the finite element computation of elasticity on NVIDIA GPUs using compute unified device architecture (CUDA). Sliced block ELLPACK (SBELL) format is introduced to store sparse matrix arising from finite element discretization of elasticity with fewer padding zeros than traditional ELLPACK-based formats. Polynomial preconditioning methods have been investigated both in convergence and running time. From the overall performance, the least-squares (L-S) polynomial method is chosen as a preconditioner in PCG solver to finite element equations derived from elasticity for its best results on different example meshes. In the PCG solver, mixed precision algorithm is used not only to reduce the overall computational, storage requirements and bandwidth but to make full use of the capacity of the GPU devices. With SBELL format and mixed precision algorithm, the GPU-based L-S preconditioned CG can get a speedup of about 7–9 to CPU-implementation.
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9

Yang, Jucheng, Xiaojing Wang, Shujie Han, Jie Wang, Dong Sun Park, and Yuan Wang. "Improved Real-Time Facial Expression Recognition Based on a Novel Balanced and Symmetric Local Gradient Coding." Sensors 19, no. 8 (April 22, 2019): 1899. http://dx.doi.org/10.3390/s19081899.

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In the field of Facial Expression Recognition (FER), traditional local texture coding methods have a low computational complexity, while providing a robust solution with respect to occlusion, illumination, and other factors. However, there is still need for improving the accuracy of these methods while maintaining their real-time nature and low computational complexity. In this paper, we propose a feature-based FER system with a novel local texture coding operator, named central symmetric local gradient coding (CS-LGC), to enhance the performance of real-time systems. It uses four different directional gradients on 5 × 5 grids, and the gradient is computed in the center-symmetric way. The averages of the gradients are used to reduce the sensitivity to noise. These characteristics lead to symmetric of features by the CS-LGC operator, thus providing a better generalization capability in comparison to existing local gradient coding (LGC) variants. The proposed system further transforms the extracted features into an eigen-space using a principal component analysis (PCA) for better representation and less computation; it estimates the intended classes by training an extreme learning machine. The recognition rate for the JAFFE database is 95.24%, whereas that for the CK+ database is 98.33%. The results show that the system has advantages over the existing local texture coding methods.
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Smistad, Erik, and Frank Lindseth. "Multigrid gradient vector flow computation on the GPU." Journal of Real-Time Image Processing 12, no. 3 (October 30, 2014): 593–601. http://dx.doi.org/10.1007/s11554-014-0466-2.

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Дисертації з теми "GRADIENT COMPUTATION"

1

Qiao, Lei Ph D. Massachusetts Institute of Technology. "Variational constitutive updates for strain gradient isotropic plasticity." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55079.

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Анотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 93-96).
In the past decades, various strain gradient isotropic plasticity theories have been developed to describe the size-dependence plastic deformation mechanisms observed experimentally in micron-indentation, torsion, bending and thin-film bulge tests in metallic materials. Strain gradient plasticity theories also constitute a convenient device to introduce ellipticity in the differential equations governing plastic deformation in the presence of softening. The main challenge to the numerical formulations is that the effective plastic strain, a local internal variable in the classic isotropic plasticity theory, is now governed by the partial differential equation which includes spatial derivatives. Most of the current numerical formulations are based on Aifantis' one-parameter model with a Laplacian term [Aifantis and Muhlhaus, ijss, 28:845-857, 1991]. As indicated in the paper [Fleck and Hutchinson, jmps, 49:2245-2271, 2001], one parameter is not sufficient to match the experimental data. Therefore a robust and efficient computational framework that can deal with more parameters is still in need. In this thesis, a numerical formulation based on the framework of variational constitutive updates is presented to solve the initial boundary value problem in strain gradient isotropic plasticity. One advantage of this approach compared to the mixed methods is that it avoids the need to solve for both the displacement and the effective plastic strain fields simultaneously. Another advantage of this approach is, as has been amply established for many other material models, that the solution of the problem follows a minimum principle, thus providing a convenient basis for error estimation and adaptive remeshing.
(cont.) The advantages of the framework of variational constitutive updates have already been verified in a wide class of material models including visco-elasticity, visco-plasticity, crystal plasticity and soil, however this approach has not been implemented in the strain gradient plasticity models. In this thesis, a three-parameter strain gradient isotropic plasticity model is formulated within the variational framework, which is then taken as a basis for finite element discretization. The resulting model is implemented in a computer code and exercised on the benchmark problems to demonstrate the robustness and versatility of the proposed method.
by Lei Qiao.
S.M.
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2

Damou, Merzak. "Measurement and computation of a turbulent jet in an axial pressure gradient." Thesis, University of Manchester, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305418.

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Sitta, Alessandro. "Privacy-Preserving Distributed Optimization via Obfuscated Gradient Tracking." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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As the modern world becomes increasingly digitized and interconnected, distributed systems have proven to be effective in the processing of large volumes of data. In this context, optimization techniques have become essential in an extensive range of domains. However, a major concern, regarding the privacy issue in handling sensitive data, has recently emerged. To address this privacy issue we propose a novel consensus-based privacy-preserving distributed optimization algorithm called Obfuscated Gradient Tracking. The algorithm is characterized by a balanced noise insertion method which protects private data from being revealed to others, while not affecting the result’s accuracy. Indeed, we theoretically prove that the introduced perturbations do not condition the convergence properties of the algorithm, which is proven to reach the optimal solution without compromises. Moreover, security against the widely-used honest-but-curious adversary model, is shown. Furthermore, numerical tests are performed to show the effectiveness of the novel algorithm, both in terms of privacy and convergence properties. Numerical results highlight the Obfuscated Gradient Tracking attractiveness, against standard distributed algorithms, when privacy issues are involved. Finally, we present a privacy-preserving distributed Deep Learning application developed using our novel algorithm, with the aim of demonstrating its general applicability.
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4

Al-Mudhaf, Ali F. "A feed forward neural network approach for matrix computations." Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/5010.

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A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to be construct. It should be noticed, however, that the present approach depends highly on the training algorithm of the FNN. Unfortunately, the available training methods; such as, the original Back-propagation (BP) algorithm, encounter many deficiencies when applied to matrix algebra problems; e. g., slow convergence due to improper choice of learning rates (LR). Thus, this study will focus on the development of new efficient and accurate FNN training methods. One improvement suggested to alleviate the problem of LR choice is the use of a line search with steepest descent method; namely, bracketing with golden section method. This provides an optimal LR as training progresses. Another improvement proposed in this study is the use of conjugate gradient (CG) methods to speed up the training process of the neural network. The computational feasibility of these methods is assessed on two matrix problems; namely, the LU-decomposition of both band and square ill-conditioned unsymmetric matrices and the inversion of square ill-conditioned unsymmetric matrices. In this study, two performance indexes have been considered; namely, learning speed and convergence accuracy. Extensive computer simulations have been carried out using the following training methods: steepest descent with line search (SDLS) method, conventional back propagation (BP) algorithm, and conjugate gradient (CG) methods; specifically, Fletcher Reeves conjugate gradient (CGFR) method and Polak Ribiere conjugate gradient (CGPR) method. The performance comparisons between these minimization methods have demonstrated that the CG training methods give better convergence accuracy and are by far the superior with respect to learning time; they offer speed-ups of anything between 3 and 4 over SDLS depending on the severity of the error goal chosen and the size of the problem. Furthermore, when using Powell's restart criteria with the CG methods, the problem of wrong convergence directions usually encountered in pure CG learning methods is alleviated. In general, CG methods with restarts have shown the best performance among all other methods in training the FNN for LU-decomposition and matrix inversion. Consequently, it is concluded that CG methods are good candidates for training FNN of matrix computations, in particular, Polak-Ribidre conjugate gradient method with Powell's restart criteria.
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Sautter, Rubens Andreas. "Gradient Pattern Analysis: New methodological and computational features with applications." Instituto Nacional de Pesquisas Espaciais (INPE), 2018. http://urlib.net/sid.inpe.br/mtc-m21c/2018/05.07.12.09.

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Neste trabalho é apresentado a Análise de Padrões Gradientes (no inglês GPA), um formalismo que descreve operadores para a análise de matrizes, por meio da simetria. Com o objetivo de analisar bases de dados extensas, neste trabalho é proposto o refinamento da versão mais popular do GPA, a respeito da medida e da complexidade computacional. Neste estudo é apresentado todos os momentos gradiente, e testado o primeiro e segundo momento gradiente (respectivamente G1 e G2). A fim de testar o refinamento das técnicas G1 e G2 é apresentado os casos de estudos: (i) um estudo de caso dinâmicos em Grade de Mapas Acoplados (no inglês CML) e (ii) um estudo de caso estático em Morfologia de galáxias. Em relação aplicação (i), duas transições de estado do sistema são apresentados: quebra de simetria e sincronização. Em relação a aplicação (ii), foi desenvolvido um pipeline de análise não paramétrica de galáxias conhecido como CyMorph. O pipeline apresentado incorpora uma versão aprimorada das técnicas de análise morfologica, G1 e G2. O objetivo principal do CyMorph dentro do escopo do projeto de pesquisa é classificar galáxias entre elipticas (early-type) e espirais (late-type). Analisando o desempenho da técnica de GPA frente as técnicas tradicionais de morfologia, observou-se que G2 é o segundo melhor parâmetro morfométrico no conjunto apresentado.
In this work it is presented the Gradient Pattern Analysis (GPA), a formalism that describes operators for analysis of spatially extended system, concerning its asymmetry. Aiming to work with large datasets, it is proposed improvements to the most popular version of GPA, with respect to the metric measurement and computational efficiency. We also review and explore the gradient moments, and propose two new operators. In order to validate the implementation of the operators G1 and G2, the following study cases are presented: (i) a dynamical study case in Coupled Map Lattices (CML), and (ii) a static case study in Galaxy Morphology. With respect to application (i), we analyze two system transitions: symmetry breaking and synchronization. Concerning the application (ii), it is presented a system of galaxy morphometrics named CyMorph, which has an important role on a project for studying the galaxies formation and evolution. The aim of CyMorph is to classify galaxies, between early-type and late-type using non-parametric morphometrics. G1 and G2 were integrated to CyMorph. We observe that G2 is the second-best morphometric in a system with 10 metrics.
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Chauffour, Marie-Laure. "Shock-based waverider design with pressure gradient corrections and computational simulations." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1829.

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Анотація:
Thesis (M.S.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Dept. of Aerospace Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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7

Fischer, Paul [Verfasser], and Paul [Akademischer Betreuer] Steinmann. "C1 Continuous Methods in Computational Gradient Elasticity / Paul Fischer. Betreuer: Paul Steinmann." Erlangen : Universitätsbibliothek der Universität Erlangen-Nürnberg, 2011. http://d-nb.info/1015783635/34.

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8

Miles, Alexander, William Duncan, Brian Klug, and Colton Holmes. "Rapid Prototyped Terahertz-Domain Gradient Index Optics: Computational Design, Simulation, and Manufacture." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595744.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
There are a myriad of applications for terahertz radiation: security, military radar, product inspection, and telecommunications. These require manipulation of the radiation beyond simple transmission and detection, namely refraction: focusing, defocusing, and collimation. The current state of the art fabrication of terahertz lenses is an expensive and time consuming processes; involving high purity semiconductors and months of lead time. Our project focused on demonstrating that an inexpensive and quick process could reduce the production investment required by more than three orders of magnitude. This process is based on fabrication using a novel gradient index structure produced with polymer-jetting rapid-prototyping machine.
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9

Thill, Serge. "A computational analysis of the gradient navigation strategies of the nematode Caenorhabditis elegans." Thesis, University of Leicester, 2008. http://hdl.handle.net/2381/4014.

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In the present thesis, we apply computational methods to the study of animal behaviour. Specifically, we are interested in the gradient navigation strategies of C. elegans, for which we show that there are many interesting questions that have not yet been answered by existing research. In order to study the behaviour of C. elegans, we first develop a range of tools to help us do so. We base a large part of our work on Markov-like models of behaviour and since these are not Markovian in the strict sense (limiting the analytical tools which can be used to study their behaviour), we first present a possible transformation from a Markov-like model with variable transition probabilities into a strictly Markovian model. We next present a framework for studying the behaviour of behavioural models which is not restricted to the work presented here but is likely to find general use in behavioural studies. Using these tools, we then analyse the chemotactic behaviour of C. elegans, showing that we can adequately explain most features of this behaviour using energy-efficiency considerations. We also show that the main behavioural strategy, so-called pirouettes is likely to be caused by an inability to sample the environment during a turn and that the animal my not be acting upon gradient information while reversing. Finally, we investigate the deterministic isotherm tracking strategy displayed by C. elegans. We develop a computational model for this behaviour which is able to reproduce all of the main features of C. elegans isotherm tracking and we propose a candidate neural circuit which might encode this strategy. Additionally, we briefly discuss the use of stochastic strategies by the animal when moving towards its preferred temperature. In summary, the work presented here therefore provides contributions to two major fields: we extend the methodology available for behavioural analysis in ethology and we contribute a number of insights and advancements to the field of C. elegans research.
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10

Norris, Michael K. "INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1629.

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This work presents improvements to Monte Carlo Localization (MCL) for a mobile robot using computer vision. Solutions to the localization problem aim to provide fine resolution on location approximation, and also be resistant to changes in the environment. One such environment change is the kidnapped/teleported robot problem, where a robot is suddenly transported to a new location and must re-localize. The standard method of "Augmented MCL" uses particle filtering combined with addition of random particles under certain conditions to solve the kidnapped robot problem. This solution is robust, but not always fast. This work combines Histogram of Oriented Gradients (HOG) computer vision with particle filtering to speed up the localization process. The major slowdown in Augmented MCL is the conditional addition of random particles, which depends on the ratio of a short term and long term average of particle weights. This ratio does not change quickly when a robot is kidnapped, leading the robot to believe it is in the wrong location for a period of time. This work replaces this average-based conditional with a comparison of the HOG image directly in front of the robot with a cached version. This resulted in a speedup ranging from from 25.3% to 80.7% (depending on parameters used) in localization time over the baseline Augmented MCL.
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Книги з теми "GRADIENT COMPUTATION"

1

Greenbaum, Anne. Predicting the behavior of finite precision Lanczos and conjugate gradient computations. New York: Courant Institute of Mathematical Sciences, New York University, 1991.

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2

The Lanczos and conjugate gradient algorithms: From theory to finite precision computations. Philadelphia: Society for Industrial and Applied Mathematics, 2006.

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3

Fu, Michael. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Boston, MA: Springer US, 1997.

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4

G, Hinshaw, and United States. National Aeronautics and Space Administration., eds. Three-point correlations in COBE DMR maps. [Washington, DC: National Aeronautics and Space Administration, 1995.

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5

1929-, Chung T. J., and United States. National Aeronautics and Space Administration., eds. Flowfield-dependent mixed explicit-implicit (FDMEI) algorithm for computational fluid dynamics: Final report ... [Washington, DC: National Aeronautics and Space Administration, 1997.

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6

P, Leonard B., and United States. National Aeronautics and Space Administration., eds. A modified mixing length turbulence model for zero and adverse pressure gradients. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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7

Conley, J. M. A modified mixing length turbulence model for zero and adverse pressure gradients. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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8

D, Simon Horst, Tang Wei-Pai, and Research Institute for Advanced Computer Science (U.S.), eds. Spectral ordering techniques for incomplete LU preconditioners for CG methods. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1995.

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9

Wang, Yan Ming. Downward continuation of the free-air gravity anomalies to the ellipsoid using the gradient solution, Poisson's integral and terrain correction-numerical comparison and the computations. Columbus, Ohio: Dept. of Geodetic Science and Surveying, Ohio State University, 1988.

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10

1956-, Volakis John Leonidas, and United States. National Aeronautics and Space Administration., eds. A finite element-boundary integral method for electromagnetic scattering. Ann Arbor, Mich: University of Michigan, Radiation Laboratory, Dept. of Electrical Engineering and Computer Science, 1992.

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Частини книг з теми "GRADIENT COMPUTATION"

1

Moukalled, F., L. Mangani, and M. Darwish. "Gradient Computation." In The Finite Volume Method in Computational Fluid Dynamics, 273–302. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16874-6_9.

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2

Sabbagh, Harold A., R. Kim Murphy, Elias H. Sabbagh, Liming Zhou, and Russell Wincheski. "A Bilinear Conjugate-Gradient Inversion Algorithm." In Scientific Computation, 3–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67956-9_1.

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3

Rall, Louis B. "Gradient Computation by Matrix Multiplication." In Applied Mathematics and Parallel Computing, 233–40. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-99789-1_16.

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Jiang, Bo-nan. "The Element-by-Element Conjugate Gradient Method." In Scientific Computation, 385–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03740-9_15.

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Šolcová, Alena. "The Founders of the Conjugate Gradient Method." In Scientific Computation, 3–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18560-1_1.

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Křížek, Michal, and Sergey Korotov. "Geometric Interpretations of Conjugate Gradient and Related Methods." In Scientific Computation, 25–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18560-1_3.

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Vermolen, Fred, Kees Vuik, and Guus Segal. "Deflation in Preconditioned Conjugate Gradient Methods for Finite Element Problems." In Scientific Computation, 103–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18560-1_7.

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8

Ruppel, Philipp, and Jianwei Zhang. "Efficent Gradient Propagation for Robot Control and Learning." In Cognitive Computation and Systems, 237–46. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2789-0_20.

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Bücker*, H. Martin, and Manfred Sauren. "Reducing Global Synchronization in the Biconjugate Gradient Method." In Parallel Numerical Computation with Applications, 63–76. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5205-5_5.

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Sachs, Ekkehard W., and Matthias Schu. "Gradient Computation for Model Calibration with Pointwise Observations." In Control and Optimization with PDE Constraints, 117–36. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0631-2_7.

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Тези доповідей конференцій з теми "GRADIENT COMPUTATION"

1

Křivánek, Jaroslav, Pascal Gautron, Kadi Bouatouch, and Sumanta Pattanaik. "Improved radiance gradient computation." In ACM SIGGRAPH 2008 classes. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401132.1401229.

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Křivánek, Jaroslav, Pascal Gautron, Kadi Bouatouch, and Sumanta Pattanaik. "Improved radiance gradient computation." In the 21st spring conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1090122.1090148.

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3

Skilling, John, Paul M. Goggans, and Chun-Yong Chan. "Conjugate Gradient for Bayesian Computation." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2009. http://dx.doi.org/10.1063/1.3275624.

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4

Son, Kyungrak, and Aditya Ramamoorthy. "Coded matrix computation with gradient coding." In 2023 IEEE International Symposium on Information Theory (ISIT). IEEE, 2023. http://dx.doi.org/10.1109/isit54713.2023.10206996.

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Boyang Li, Yew-Soon Ong, Minh Nghia Le, and Chi Keong Goh. "Memetic Gradient Search." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631187.

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Li, Vladimir, Hui Wang, Ilya Tsvankin, Esteban Diaz, and Tariq Alkhalifah. "Gradient computation for VTI acoustic wavefield tomography." In SEG Technical Program Expanded Abstracts 2016. Society of Exploration Geophysicists, 2016. http://dx.doi.org/10.1190/segam2016-13967436.1.

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Kera, Hiroshi. "Border Basis Computation with Gradient-Weighted Normalization." In ISSAC '22: International Symposium on Symbolic and Algebraic Computation. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3476446.3535476.

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Moraes, R., J. R. P. Rodrigues, H. Hajibeygi, and J. D. Jansen. "Multiscale Gradient Computation for Subsurface Flow Models." In ECMOR XV - 15th European Conference on the Mathematics of Oil Recovery. Netherlands: EAGE Publications BV, 2016. http://dx.doi.org/10.3997/2214-4609.201601891.

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Xu, Zhiqiang, Xin Cao, and Xin Gao. "Convergence Analysis of Gradient Descent for Eigenvector Computation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/407.

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Анотація:
We present a novel, simple and systematic convergence analysis of gradient descent for eigenvector computation. As a popular, practical, and provable approach to numerous machine learning problems, gradient descent has found successful applications to eigenvector computation as well. However, surprisingly, it lacks a thorough theoretical analysis for the underlying geodesically non-convex problem. In this work, the convergence of the gradient descent solver for the leading eigenvector computation is shown to be at a global rate O(min{ (lambda_1/Delta_p)^2 log(1/epsilon), 1/epsilon }), where Delta_p=lambda_p-lambda_p+1>0 represents the generalized positive eigengap and always exists without loss of generality with lambda_i being the i-th largest eigenvalue of the given real symmetric matrix and p being the multiplicity of lambda_1. The rate is linear at (lambda_1/Delta_p)^2 log(1/epsilon) if (lambda_1/Delta_p)^2=O(1), otherwise sub-linear at O(1/epsilon). We also show that the convergence only logarithmically instead of quadratically depends on the initial iterate. Particularly, this is the first time the linear convergence for the case that the conventionally considered eigengap Delta_1= lambda_1 - lambda_2=0 but the generalized eigengap Delta_p satisfies (lambda_1/Delta_p)^2=O(1), as well as the logarithmic dependence on the initial iterate are established for the gradient descent solver. We are also the first to leverage for analysis the log principal angle between the iterate and the space of globally optimal solutions. Theoretical properties are verified in experiments.
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Iandola, Forrest N., Matthew W. Moskewicz, and Kurt Keutzer. "libHOG: Energy-Efficient Histogram of Oriented Gradient Computation." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.205.

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Звіти організацій з теми "GRADIENT COMPUTATION"

1

Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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Анотація:
The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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