The causal bootstrap computes rigorous bounds on smeared spectral functions from non-perturbative Euclidean data by optimizing over the convex set of compatible positive spectral densities and reducing dual problems to semidefinite programs for certain kernels.
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Finite-N bootstrap yields N-independent bounds for matrix models but N-dependent novel bounds on the two-point function versus quartic coupling for tensor models.
A semidefinite programming bootstrap is formulated for Euclidean two-point correlators in quantum mechanics, yielding rigorous bounds and low-lying spectrum extraction in the ungauged one-matrix model.
A finite-dimensional regularization of the master field enables direct numerical computation of large-N matrix models in both Euclidean and Minkowski signatures while reproducing known solutions in simple test cases.
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The Causal Bootstrap: Bounding Smeared Spectral Functions from Non-Perturbative Euclidean Data
The causal bootstrap computes rigorous bounds on smeared spectral functions from non-perturbative Euclidean data by optimizing over the convex set of compatible positive spectral densities and reducing dual problems to semidefinite programs for certain kernels.
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Finite-$N$ Bootstrap Constraints in Matrix and Tensor Models
Finite-N bootstrap yields N-independent bounds for matrix models but N-dependent novel bounds on the two-point function versus quartic coupling for tensor models.
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Bootstrapping Euclidean Two-point Correlators
A semidefinite programming bootstrap is formulated for Euclidean two-point correlators in quantum mechanics, yielding rigorous bounds and low-lying spectrum extraction in the ungauged one-matrix model.
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Regularized Master-Field Approximation for Large-$N$ Reduced Matrix Models
A finite-dimensional regularization of the master field enables direct numerical computation of large-N matrix models in both Euclidean and Minkowski signatures while reproducing known solutions in simple test cases.