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Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction. [PDF]
Hong T +4 more
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Symplectic physics-embedded learning via Lie groups Hamiltonian formulation for serial manipulator dynamics prediction. [PDF]
Wang F, Chen L, Ding J.
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Soft-Community Kernel Rényi Spectrum for Semantic Uncertainty Estimation in Large Language Models. [PDF]
Li Z, Du J.
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Transphyseal Proximal Humeral Aneurysmal Bone Cyst with Pathologic Fracture in a Child: A Case Report. [PDF]
Li T, Wang J, Zhang Q, Zhang Z.
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On the existence of a positive definite solution of the matrix equation
International Journal of Computer Mathematics, 2001In this paper, an efficient and numerically stable algorithm for computing the positive definite solution of the nonlinear equation is proposed. Some properties of the solution are discussed as well as the sufficient conditions for the existence are obtained.
Mohamed Ramadan, Salah M El-Sayed
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Computing the Extremal Positive Definite Solutions of a Matrix Equation
SIAM Journal of Scientific Computing, 1996An implementation of a well-known algorithm is proposed for finding extremal positive definite solutions of the matrix equation \(X+A^*X^{-1}A=I\). The convergence rate is analyzed. Then a new algorithm is presented. This algorithm avoids matrix inversions.
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Multisplitting of a Symmetric Positive Definite Matrix
SIAM Journal on Matrix Analysis and Applications, 1990The author considers parallel iterative methods for systems of linear equations \(Au=d\), \(A=A^*>0\). If \(A=B_ k-C_ k\), \(k=1,...,K\) are given splittings of the matrix A then the iterative methods can be written in the form \(u^{n+1}=\sum_{k}D_ kB_ k^{-1}C_ ku^ n+\sum_{k}D_ kB_ k^{-1}d\) where \(D_ k\geq 0\) are diagonal matrices and \(\sum_{k}D_ k=
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