Copositive matrices with nondecreasing off-diagonal entries admit a PSD plus nonnegative decomposition, which implies exactness of a natural relaxation for separable quadratic optimization over the simplex.
A multilinear sin gu- lar value decomposition
8 Pith papers cite this work, alongside 4,180 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
Explicit support and gauge functions characterize the correlation sets in the (2,m,2) Bell scenario for three state spaces, yielding optimal witnesses for entanglement and beyond-quantum correlations with noise robustness thresholds.
Tensor-network reformulation of strong-coupling QCD with staggered fermions at nonzero μ, yielding analytical results on 2×2 lattices up to β^4.
H-SAL erases latent concepts from text profiles using self-descriptions as implicit debiasing signals and shows competitive performance on a new multi-domain Stack Exchange helpfulness benchmark.
Decoupling local and synchronization transitions yields a linearly convergent MTTA algorithm that is accelerated to quadratic convergence and represented in tensor-train format, enabling computation on systems with up to billions of states.
An a-priori error estimate for space-time Galerkin POD is derived for linear parabolic evolution problems, bounding the FOM-ROM error by singular-value tail sums and problem-specific constants.
Structured 3D-SVD delivers reconstruction quality near Tucker decomposition with shorter runtimes and beats CPD on accuracy and speed for biological 3D image compression and progressive reconstruction.
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
citing papers explorer
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Copositive Matrices with Ordered Off-Diagonal Entries
Copositive matrices with nondecreasing off-diagonal entries admit a PSD plus nonnegative decomposition, which implies exactness of a natural relaxation for separable quadratic optimization over the simplex.
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Dualistic operational characterization of device-dependent correlation sets via convex analysis in the $(2,m,2)$ Bell scenario
Explicit support and gauge functions characterize the correlation sets in the (2,m,2) Bell scenario for three state spaces, yielding optimal witnesses for entanglement and beyond-quantum correlations with noise robustness thresholds.
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Tensor-network formulation of QCD in the strong-coupling expansion
Tensor-network reformulation of strong-coupling QCD with staggered fermions at nonzero μ, yielding analytical results on 2×2 lattices up to β^4.
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Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profiles
H-SAL erases latent concepts from text profiles using self-descriptions as implicit debiasing signals and shows competitive performance on a new multi-domain Stack Exchange helpfulness benchmark.
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Tensor methods for the computation of MTTA in large systems of loosely interconnected components
Decoupling local and synchronization transitions yields a linearly convergent MTTA algorithm that is accelerated to quadratic convergence and represented in tensor-train format, enabling computation on systems with up to billions of states.
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A-priori error estimation for space-time Galerkin POD for linear evolution problems
An a-priori error estimate for space-time Galerkin POD is derived for linear parabolic evolution problems, bounding the FOM-ROM error by singular-value tail sums and problem-specific constants.
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Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
Structured 3D-SVD delivers reconstruction quality near Tucker decomposition with shorter runtimes and beats CPD on accuracy and speed for biological 3D image compression and progressive reconstruction.
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MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.