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Random Matrices and complexity of Spin Glasses
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Random Matrices and complexity of Spin Glasses
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We give an asymptotic evaluation of the complexity of spherical p-spin spin-glass models via random matrix theory. This study enables us to obtain detailed information about the bottom of the energy landscape, including the absolute minimum (the ground state), the other local minima, and describe an interesting layered structure of the low critical values for the Hamiltonians of these models. We also show that our approach allows us to compute the related TAP-complexity and extend the results known in the physics literature. As an independent tool, we prove a LDP for the k-th largest eigenvalue of the GOE, extending the results of Ben Arous, Dembo and Guionnett (2001).
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Cited by 1 Pith paper
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Quantum estimates for classical polynomial optimization
A quantum-variational matrix method is proposed for bounding homogeneous polynomials, with converging numerical tensor-eigenvalue estimates and a new conjectured inequality for Biasi's resonant Hamiltonians.
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