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Showing 1 to 10 of 10 for “"Matrix computation"”.
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Preconditioning For Matrix Computation
… to solve. The two central subjects of numerical matrix computations are LIN-SOLVE, that is, the solution of linear systems of equations and EIGEN-SOLVE, that is, the approximation of the eigenvalues and eigenvectors of a matrix. We focus on the former subject of LIN-SOLVE and show an application …
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Analyses of the Lanczos Algorithm and of the Approximation Problem in Richardson's Method
Two algorithms of use in sparse matrix computation are studied. The rounding errors of the computational Lanczos algorithm are examined in order to account for the differences between the ideal and the machine-operator quantities. The observed behavior of these errors is explained by means of a …
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Active control of floor vibrations
… test floor. This model is studied, using a matrix computation software, to evaluate the effectiveness of the control scheme. The experimental component of the research serves two purposes. The first is the verification of the system behavior assumed in the analytical component of the …
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LOW RANK AND SPARSE MODELING FOR DATA ANALYSIS
… Since the general rank minimization problem is computationally NP-hard, the convex relaxation of original problem is often solved. One popular heuristic method is to use the nuclear norm to approximate the rank of a matrix. Despite the success of nuclear norm minimization in capturing the low …
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A mesh architecture for data management of matrix computations.
… applications like these rely on high-speed matrix computations. Although the computational ability of general-purpose computer architectures is growing, some numerically intensive calculations (such as those above) may benefit from specialised matrix processing hardware. Work in this thesis …
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Algebraic approaches for coded caching and distributed computing
… and straggler mitigation within distributed computation.</p> <p>Caching is a popular technique for facilitating large scale content delivery over the Internet. Traditionally, caching operates by storing popular content closer to the end users. Recent work within the domain of information …
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Efficient machine learning: models and accelerations
… and large model sizes also demand to increase computational capability and memory requirements. In order to achieve higher scalability, performance, and energy efficiency for deep learning systems, two orthogonal research and development trends have attracted enormous interests. The first trend …
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Refined theories and Discontinuous Galerkin methods for the analysis of multilayered composite structures
… can provide very detailed results at an high computational cost. This approach is well suited for small parts but cannot be applied to complex structures or for damage-tolerance analysis. Thus, accurate and computationally efficient models are required for the design and analysis of …