The tensor spectral threshold decision problem is ∃R-hard via an explicit polynomial-time reduction from bounded quartic equality feasibility.
De Silva and L.-H
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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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Tensor Spectral Threshold is $\exists\mathbb{R}$-Hard
The tensor spectral threshold decision problem is ∃R-hard via an explicit polynomial-time reduction from bounded quartic equality feasibility.
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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.