Pages that link to "Item:Q1931773"
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The following pages link to Monotonically convergent algorithms for symmetric tensor approximation (Q1931773):
Displaying 14 items.
- Symmetric tensor decomposition by an iterative eigendecomposition algorithm (Q738956) (← links)
- A locally convergent Jacobi iteration for the tensor singular value problem (Q784616) (← links)
- A Riemannian gradient ascent algorithm with applications to orthogonal approximation problems of symmetric tensors (Q2085661) (← links)
- Alternate algorithms to most referenced techniques of numerical optimization to solve the symmetric rank-\(R\) approximation problem of symmetric tensors (Q2087499) (← links)
- Time integration of symmetric and anti-symmetric low-rank matrices and Tucker tensors (Q2192592) (← links)
- Numerical optimization for symmetric tensor decomposition (Q2349123) (← links)
- An equi-directional generalization of adaptive cross approximation for higher-order tensors (Q2448363) (← links)
- On the best rank-1 approximation of higher-order supersymmetric tensors (Q2784384) (← links)
- Random Projections for Low Multilinear Rank Tensors (Q2806290) (← links)
- Symmetric rank-1 approximation of symmetric high-order tensors (Q5210746) (← links)
- Numerical Computation for Orthogonal Low-Rank Approximation of Tensors (Q5237898) (← links)
- A Global Convergence Analysis for Computing a Symmetric Low-Rank Orthogonal Approximation (Q5888375) (← links)
- On an algorithm converging to hyperstochastic tensors (Q6085699) (← links)
- Scalable symmetric Tucker tensor decomposition (Q6623664) (← links)
