Results 11 to 20 of about 429,972 (288)
Controlled precision QUBO-based algorithm to compute eigenvectors of symmetric matrices.
PLoS ONE, 2022 We describe an algorithm to compute the extremal eigenvalues and corresponding eigenvectors of a symmetric matrix which is based on solving a sequence of Quadratic Binary Optimization problems.
Benjamin Krakoff +2 more
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PLoS ONE, 2023 To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization ...
Chuang Zhang +4 more
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Symmetric Nonnegative Matrix Factorization Based on Box-Constrained Half-Quadratic Optimization
IEEE Access, 2020 Nonnegative Matrix Factorization (NMF) based on half-quadratic (HQ) functions was proven effective and robust when dealing with data contaminated by continuous occlusion according to the half-quadratic optimization theory.
Bo-Wei Chen
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Symmetrization and optimal control for elliptic equations [PDF]
Proceedings of the American Mathematical Society, 1987 We consider an optimal control problem where u ( x ) u(x) satisfies − div ( H ( x ) ∇ u ) = 1 - \operatorname {div}(H(x)\nabla u) = 1 in Ω \Omega and H ( x
Voas, Charles, Yaniro, Daniel
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Harvester: Influence Optimization in Symmetric Interaction Networks [PDF]
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016 The problem of optimizing influence diffusion ina network has applications in areas such as marketing, diseasecontrol, social media analytics, and more. In all cases, an initial setof influencers are chosen so as to optimize influence propagation.While a lot of research has been devoted to the influencemaximization problem, most solutions proposed to ...
Ivanov, Sergei, Karras, Panagiotis
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Optimizing the HSX stellarator for microinstability by coil-current adjustments
Nuclear Fusion, 2023 The optimization of helically symmetric experiment (HSX) for reduced microinstability has been achieved by examining a large set of configurations within a neighborhood of the standard operating configuration.
M.J. Gerard +8 more
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Optimizing Gershgorin for symmetric matrices [PDF]
Linear Algebra and its Applications, 2019 The Gershgorin Circle Theorem is a well-known and efficient method for bounding the eigenvalues of a matrix in terms of its entries. If $A$ is a symmetric matrix, by writing $A = B + x{\bf 1}$, where ${\bf 1}$ is the matrix with unit entries, we consider the problem of choosing $x$ to give the optimal Gershgorin bound on the eigenvalues of $B$, which ...
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Electronics Letters, 2023 In this letter, an analytical method for the beampattern synthesis of symmetric nonuniform array is proposed. This method consists of two steps. In the first step, it acquires a real symmetric excitation by the convex optimization method to attain a ...
Fei Shi, Mengkai Hu, Xiuquan Dou
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Generalized symmetric ADMM for separable convex optimization [PDF]
Computational Optimization and Applications, 2017 The Alternating Direction Method of Multipliers (ADMM) has been proved to be effective for solving separable convex optimization subject to linear constraints. In this paper, we propose a Generalized Symmetric ADMM (GS-ADMM), which updates the Lagrange multiplier twice with suitable stepsizes, to solve the multi-block separable convex programming. This
Jianchao Bai +3 more
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Numerical optimization for symmetric tensor decomposition [PDF]
Mathematical Programming, 2015 We consider the problem of decomposing a real-valued symmetric tensor as the sum of outer products of real-valued vectors. Algebraic methods exist for computing complex-valued decompositions of symmetric tensors, but here we focus on real-valued decompositions, both unconstrained and nonnegative, for problems with low-rank structure.
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