A multipoint perturbation formula for eigenvalue problems
- URL: http://arxiv.org/abs/2305.08151v1
- Date: Sun, 14 May 2023 13:06:09 GMT
- Title: A multipoint perturbation formula for eigenvalue problems
- Abstract summary: If the corresponding eigenmodes of several nearby Hamiltonians are known, standard perturbation theory cannot simultaneously use all this knowledge to provide a better approximation.
We derive a formula enabling such an approximation result, and provide numerical examples for which this method is more competitive than standard perturbation theory.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Standard perturbation theory of eigenvalue problems consists of obtaining
approximations of eigenmodes in the neighborhood of a Hamiltonian where the
corresponding eigenmode is known.
Nevertheless, if the corresponding eigenmodes of several nearby Hamiltonians
are known, standard perturbation theory cannot simultaneously use all this
knowledge to provide a better approximation. We derive a formula enabling such
an approximation result, and provide numerical examples for which this method
is more competitive than standard perturbation theory.
Related papers
- Error bounds for splitting methods in unitary problems [45.88028371034407]
We present a systematic analysis of both local and global errors arising from arbitrary splitting methods applied to unitary problems.<n>The first is expressed in terms of operator norms, while the second is formulated using norms of commutators and can, under suitable assumptions, be extended to certain classes of operators.
arXiv Detail & Related papers (2026-04-01T15:33:05Z) - On the Sample Complexity of Learning for Blind Inverse Problems [8.091533689145908]
Blind inverse problems arise in many experimental settings where the forward operator is partially or entirely unknown.<n>Data-driven approaches have been proposed to address blind inverse problems, demonstrating strong empirical performance and adaptability.<n>We shed light on learning in blind inverse problems within the simplified yet insightful framework of Linear Minimum Mean Square Estimators.
arXiv Detail & Related papers (2025-12-29T11:53:37Z) - Towards Spectral Convergence of Locally Linear Embedding on Manifolds with Boundary [0.0]
We study the eigenvalues and eigenfunctions of a differential operator that governs the behavior of the unsupervised learning algorithm known as Locally Linear Embedding.<n>We show that a natural regularity condition on the eigenfunctions imposes a consistent boundary condition and use the Frobenius method to estimate pointwise behavior.
arXiv Detail & Related papers (2025-01-16T14:45:53Z) - Entanglement Hamiltonian and orthogonal polynomials [0.0]
We study the entanglement Hamiltonian for free-fermion chains with a particular form of inhomogeneity.
We show that this deformation is interpreted as a local inverse temperature and can be obtained in the continuum limit.
Using this prediction, the properly rescaled eigenvalues of the commuting operator are found to provide a very good approximation of the entanglement spectrum and entropy.
arXiv Detail & Related papers (2024-12-16T17:46:53Z) - Entrywise error bounds for low-rank approximations of kernel matrices [55.524284152242096]
We derive entrywise error bounds for low-rank approximations of kernel matrices obtained using the truncated eigen-decomposition.
A key technical innovation is a delocalisation result for the eigenvectors of the kernel matrix corresponding to small eigenvalues.
We validate our theory with an empirical study of a collection of synthetic and real-world datasets.
arXiv Detail & Related papers (2024-05-23T12:26:25Z) - Extension of exactly-solvable Hamiltonians using symmetries of Lie
algebras [0.0]
We show that a linear combination of operators forming a modest size Lie algebra can be substituted by determinants of the Lie algebra symmetries.
The new class of solvable Hamiltonians can be measured efficiently using quantum circuits with gates that depend on the result of a mid-circuit measurement of the symmetries.
arXiv Detail & Related papers (2023-05-29T17:19:56Z) - On the Importance of Gradient Norm in PAC-Bayesian Bounds [92.82627080794491]
We propose a new generalization bound that exploits the contractivity of the log-Sobolev inequalities.
We empirically analyze the effect of this new loss-gradient norm term on different neural architectures.
arXiv Detail & Related papers (2022-10-12T12:49:20Z) - Semirelativistic Potential Modelling of Bound States: Advocating Due
Rigour [0.0]
The Poincar'e-covariant quantum-field-theoretic description of bound states by the homogeneous Bethe-Salpeter equation exhibits an intrinsic complexity.
The resulting approximate outcome's reliability can be assessed by applying several rigorous constraints on the nature of the bound-state spectra.
arXiv Detail & Related papers (2022-08-17T07:02:01Z) - Uncertainty principle via variational calculus on modulation spaces [0.0]
We are led to compact localization operators with symbols in modulation spaces.
The optimal constant in these uncertainty principles is the smallest eigenvalue of the inverse of a compact localization operator.
As a by-product of our proofs we derive a generalization to mixed-norm spaces of an inequality for Wigner and Ambiguity functions due do Lieb.
arXiv Detail & Related papers (2022-06-24T20:34:54Z) - Optimal variance-reduced stochastic approximation in Banach spaces [114.8734960258221]
We study the problem of estimating the fixed point of a contractive operator defined on a separable Banach space.
We establish non-asymptotic bounds for both the operator defect and the estimation error.
arXiv Detail & Related papers (2022-01-21T02:46:57Z) - Hamiltonian simulation with random inputs [74.82351543483588]
Theory of average-case performance of Hamiltonian simulation with random initial states.
Numerical evidence suggests that this theory accurately characterizes the average error for concrete models.
arXiv Detail & Related papers (2021-11-08T19:08:42Z) - Preference learning along multiple criteria: A game-theoretic
perspective [97.94912276610002]
We generalize the notion of a von Neumann winner to the multi-criteria setting by taking inspiration from Blackwell's approachability.
Our framework allows for non-linear aggregation of preferences across criteria, and generalizes the linearization-based approach from multi-objective optimization.
We show that the Blackwell winner of a multi-criteria problem instance can be computed as the solution to a convex optimization problem.
arXiv Detail & Related papers (2021-05-05T03:23:11Z) - Optimal oracle inequalities for solving projected fixed-point equations [53.31620399640334]
We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensional subspace of the Hilbert space.
We show how our results precisely characterize the error of a class of temporal difference learning methods for the policy evaluation problem with linear function approximation.
arXiv Detail & Related papers (2020-12-09T20:19:32Z) - Gross misinterpretation of a conditionally solvable eigenvalue equation [0.0]
We solve an eigenvalue equation that appears in several papers about a wide range of physical problems.
We compare the resulting eigenvalues with those provided by the truncation condition.
In this way we prove that those physical predictions are merely artifacts of the truncation condition.
arXiv Detail & Related papers (2020-11-12T15:08:11Z) - Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets.
Part of the challenge of learning robust models lies in the influence of unobserved confounders.
We demonstrate the empirical performance of our approach on healthcare data from different modalities.
arXiv Detail & Related papers (2020-07-21T08:18:06Z) - The semiring of dichotomies and asymptotic relative submajorization [0.0]
We study quantum dichotomies and the resource theory of asymmetric distinguishability using a generalization of Strassen's theorem on preordered semirings.
We find that an variant of relative submajorization, defined on unnormalized dichotomies, is characterized by real-valued monotones that are multiplicative under the tensor product and additive under the direct sum.
arXiv Detail & Related papers (2020-04-22T14:13:26Z) - Bernstein spectral method for quasinormal modes and other eigenvalue
problems [0.0]
ttSpectralBP implements Berstein-polynomial-based pseudospectral routines for eigenvalue problems.
We demonstrate the functionalities of the package by applying it to a number of model problems in quantum mechanics and to the problem of computing scalar and gravitational quasinormal modes in a Schwarzschild background.
arXiv Detail & Related papers (2020-03-13T12:30:18Z) - On the generalization of bayesian deep nets for multi-class
classification [27.39403411896995]
We propose a new generalization bound for Bayesian deep nets by exploiting the contractivity of the Log-Sobolev inequalities.
Using these inequalities adds an additional loss-gradient norm term to the generalization bound, which is intuitively a surrogate of the model complexity.
arXiv Detail & Related papers (2020-02-23T09:05:03Z) - A Non-perturbative General Approximation Scheme (NGAS) for Interacting Quantum Systems with Perturbation Theory for Arbitrary Strength of Interaction [0.0]
Scheme is nonperturbative, yet improvable in a new formulation of perturbation theory, designated as mean field theory (T)
We apply the scheme to anharmonic interactions in one dimension employing the harmonic-approximation.
Results for the QDWO may be contrasted with those from the standard formulation of perturbation theory.
arXiv Detail & Related papers (2019-09-27T06:13:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.