Pages that link to "Item:Q1027786"
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The following pages link to Differential-geometric Newton method for the best rank-\((R _{1}, R _{2}, R _{3})\) approximation of tensors (Q1027786):
Displaying 17 items.
- On optimal low rank Tucker approximation for tensors: the case for an adjustable core size (Q496606) (← links)
- Computing laser beam paths in optical cavities: an approach based on geometric Newton method (Q727241) (← links)
- Efficient alternating least squares algorithms for low multilinear rank approximation of tensors (Q831251) (← links)
- A modified Newton's method for best rank-one approximation to tensors (Q979270) (← links)
- Greedy low-rank approximation in Tucker format of solutions of tensor linear systems (Q2000621) (← links)
- A Krylov-Schur-like method for computing the best rank-\((r_1,r_2,r_3)\) approximation of large and sparse tensors (Q2084262) (← links)
- A Riemannian gradient ascent algorithm with applications to orthogonal approximation problems of symmetric tensors (Q2085661) (← links)
- Tensor neural network models for tensor singular value decompositions (Q2307707) (← links)
- Nonlinearly preconditioned optimization on Grassmann manifolds for computing approximate Tucker tensor decompositions (Q2800432) (← links)
- A literature survey of low-rank tensor approximation techniques (Q2864808) (← links)
- GRADIENT FLOWS FOR OPTIMIZATION IN QUANTUM INFORMATION AND QUANTUM DYNAMICS: FOUNDATIONS AND APPLICATIONS (Q3585134) (← links)
- Numerical tensor calculus (Q4683918) (← links)
- On the convergence of higher-order orthogonal iteration (Q4685516) (← links)
- ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching (Q5027035) (← links)
- (Q5159466) (← links)
- RA-HOOI: rank-adaptive higher-order orthogonal iteration for the fixed-accuracy low multilinear-rank approximation of tensors (Q6549553) (← links)
- Scalable symmetric Tucker tensor decomposition (Q6623664) (← links)
