Krylov state complexity for BMN matrix model

• URL: http://arxiv.org/abs/2605.10786v1
• Date: Mon, 11 May 2026 16:20:16 GMT
• Title: Krylov state complexity for BMN matrix model
• Abstract summary: We explore Krylov complexity in the BMN matrix model following a systematic reduction of it, known as the fuzzy sphere model.<n>We present an analytical setup that allows us to calculate Lanczos coefficients in both large and small deformation limits of the matrix model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We explore Krylov complexity in the BMN matrix model following a systematic reduction of it, known as the pulsating fuzzy sphere model. We present an analytical setup that allows us to calculate Lanczos coefficients in both large and small deformation limits of the matrix model.

Related papers

• Krylov Complexity for Plane Wave Matrix Model [0.0]
  We study Krylov complexity in BMN Plane Wave Matrix Model at large mass deformation.<n>We consider various consistent reductions of the matrix model that allow us to perform a Hamiltonian analysis.
  arXiv  Detail & Related papers  (2026-05-25T17:15:34Z)
• Cramer-Rao Bounds for Laplacian Matrix Estimation [56.1214184671173]
  We derive closed-form matrix expressions for the Cramer-Rao Bound (CRB) specifically tailored to Laplacian matrix estimation.<n>We demonstrate the use of CRBs in three representative applications: (i) topology identification in power systems, (ii) graph filter identification in diffused models, and (iii) precision matrix estimation in Gaussian Markov random fields under Laplacian constraints.
  arXiv  Detail & Related papers  (2025-04-06T18:28:31Z)
• Symmetrizing the Constraints -- Density Matrix Renormalization Group for Constrained Lattice Models [0.40964539027092917]
  We develop a density matrix renormalization group (DMRG) algorithm for constrained quantum lattice models.<n>Such an implementation allows us to investigate a quantum dimer model in DMRG for any lattice geometry wrapped around a cylinder with substantial circumference.
  arXiv  Detail & Related papers  (2025-04-05T03:05:11Z)
• Data-free Weight Compress and Denoise for Large Language Models [96.68582094536032]
  We propose a novel approach termed Data-free Joint Rank-k Approximation for compressing the parameter matrices.<n>We achieve a model pruning of 80% parameters while retaining 93.43% of the original performance without any calibration data.
  arXiv  Detail & Related papers  (2024-02-26T05:51:47Z)
• Learning Graphical Factor Models with Riemannian Optimization [70.13748170371889]
  This paper proposes a flexible algorithmic framework for graph learning under low-rank structural constraints. The problem is expressed as penalized maximum likelihood estimation of an elliptical distribution. We leverage geometries of positive definite matrices and positive semi-definite matrices of fixed rank that are well suited to elliptical models.
  arXiv  Detail & Related papers  (2022-10-21T13:19:45Z)
• Low-Rank Constraints for Fast Inference in Structured Models [110.38427965904266]
  This work demonstrates a simple approach to reduce the computational and memory complexity of a large class of structured models. Experiments with neural parameterized structured models for language modeling, polyphonic music modeling, unsupervised grammar induction, and video modeling show that our approach matches the accuracy of standard models at large state spaces.
  arXiv  Detail & Related papers  (2022-01-08T00:47:50Z)
• Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
  We present a constructive algorithm for generating quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
  arXiv  Detail & Related papers  (2021-04-01T19:06:00Z)
• Bilinear Classes: A Structural Framework for Provable Generalization in RL [119.42509700822484]
  Bilinear Classes is a new structural framework which permits generalization in reinforcement learning. The framework incorporates nearly all existing models in which a sample complexity is achievable. Our main result provides an RL algorithm which has sample complexity for Bilinear Classes.
  arXiv  Detail & Related papers  (2021-03-19T16:34:20Z)
• Information-theoretic limits of a multiview low-rank symmetric spiked matrix model [19.738567726658875]
  We consider a generalization of an important class of high-dimensional inference problems, namely spiked symmetric matrix models. We rigorously establish the information-theoretic limits through the proof of single-letter formulas. We improve the recently introduced adaptive method, so that it can be used to study low-rank models.
  arXiv  Detail & Related papers  (2020-05-16T15:31:07Z)
• Estimation of sparse Gaussian graphical models with hidden clustering structure [8.258451067861932]
  We propose a model to estimate the sparse Gaussian graphical models with hidden clustering structure. We develop a symmetric Gauss-Seidel based alternating direction method of the multipliers. Numerical experiments on both synthetic data and real data demonstrate the good performance of our model.
  arXiv  Detail & Related papers  (2020-04-17T08:43:31Z)

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.