Pith. sign in

REVIEW 7 cited by

Eigenvalue and Generalized Eigenvalue Problems: Tutorial

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1903.11240 v3 pith:C55LCZBM submitted 2019-03-25 stat.ML cs.LG

Eigenvalue and Generalized Eigenvalue Problems: Tutorial

classification stat.ML cs.LG
keywords eigenvaluegeneralizedproblemsanalysiscomponentintroduceprincipalproblem
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

This paper is a tutorial for eigenvalue and generalized eigenvalue problems. We first introduce eigenvalue problem, eigen-decomposition (spectral decomposition), and generalized eigenvalue problem. Then, we mention the optimization problems which yield to the eigenvalue and generalized eigenvalue problems. We also provide examples from machine learning, including principal component analysis, kernel supervised principal component analysis, and Fisher discriminant analysis, which result in eigenvalue and generalized eigenvalue problems. Finally, we introduce the solutions to both eigenvalue and generalized eigenvalue problems.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Lattice-reflection symmetry in tensor-network renormalization group with entanglement filtering in two and three dimensions

    cond-mat.stat-mech 2025-10 unverdicted novelty 7.0

    A transposition trick is introduced to impose lattice-reflection symmetry in TNRG projective truncations and entanglement filtering, enabling extraction of scaling dimensions separately in each symmetry sector for 2D ...

  2. Back from the Future: Key-Value Cache Management by Counter-Causal Surprise

    cs.LG 2026-07 conditional novelty 6.0

    Past tokens that the model can predict from their future context are evicted from the KV cache, judged by a counter-causal attention pass that reuses cached keys and values.

  3. ToxiREX: A Dataset on Toxic REasoning in ConteXt

    cs.CL 2026-06 unverdicted novelty 6.0

    ToxiREX is a new dataset of 128k Reddit comments in six languages with hierarchical annotations for implicit toxicity in conversational context based on an existing reasoning schema.

  4. Implementing Fluid Antennas in the Beamspace: Performance Evaluation and Codebook Design

    eess.SP 2026-05 unverdicted novelty 6.0

    Metasurface-based fluid antennas outperform conceptual fluid antennas in interference-heavy multi-user scenarios by exploiting projection onto the interference null space.

  5. RAYEN: Imposition of Hard Convex Constraints on Neural Networks

    cs.LG 2023-07 unverdicted novelty 6.0

    RAYEN enforces hard convex constraints (linear, quadratic, SOC, LMI) on neural networks with negligible overhead while guaranteeing satisfaction at all times.

  6. Lattice and PT symmetries in tensor-network renormalization group: Case study of a hard-square lattice gas model

    cond-mat.stat-mech 2026-03 conditional novelty 5.0

    A tensor-network RG scheme that preserves lattice rotation/reflection and PT symmetries is formulated and validated on the hard-square lattice gas, yielding improved critical-point and scaling-dimension estimates.

  7. Foundations of Riemannian Geometry for Riemannian Optimization: A Monograph with Detailed Derivations

    math.DG 2026-05 unverdicted novelty 2.0

    The monograph organizes and derives classical Riemannian geometry structures explicitly in coordinate and matrix form for direct use in optimization algorithms on nonlinear manifolds.