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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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64129193257 · May 202619922001200920172026
48 results for Perron-Frobenius theory

Using an idea of Doug Lind, we give a lower bound for the Perron-Frobenius degree of a Perron number that is not totally-real. As an application, we prove that there are cubic Perron numbers whose Perron-Frobenius degrees are arbitrary large; a result known to Lind, McMullen and Thurston. A similar result is proved for…

2017-11-18abs ↗pdf ↗

Deep learning framework for kernel methods using RKHM and Perron-Frobenius operators.

problem Kernel methods in deep learning with potential overfitting issues.
method Combining RKHM and Perron-Frobenius operator to derive a new Rademacher bound and analyze deep kernel methods.
result Theoretical interpretation of benign overfitting and milder dependency on output dimension.

Study on self-consuming generative models with diverse human curation, focusing on convergence and stability.

problem Analyzing self-consuming generative models with heterogeneous human curation.
method Investigates the asymptotic behavior of retraining dynamics using nonlinear Perron--Frobenius theory and Banach contraction mapping.
result Improves convergence results and provides stability and non-stability analyses for the model.

IGNN captures long-range graph dependencies using fixed-point equations.

problem Limited GNN ability to capture long-range graph dependencies.
method Fixed-point equilibrium equations involving implicitly defined state vectors, leveraging Perron-Frobenius theory and projected gradient descent.
result IGNN consistently captures long-range dependencies and outperforms state-of-the-art GNNs.

Fixed points of nonnegative neural networks are analyzed using fixed point theory.

problem Analyzing fixed points in nonnegative neural networks.
method Fixed point theory, nonlinear Perron-Frobenius theory, monotonic and scalable mappings.
result Conditions for the existence of fixed points in nonnegative neural networks are provided.

We prove that non-elementary hyperbolic groups grow exponentially more quickly than their infinite index quasiconvex subgroups. The proof uses the classical tools of automatic structures and Perron-Frobenius theory. We also extend the main result to relatively hyperbolic groups and cubulated groups. These extensions us…

2016-02-25abs ↗pdf ↗

A pseudo-Anosov surface automorphism φφ has associated to it an algebraic unit λφλ_φ called the dilatation of φφ. It is known that in many cases λφλ_φ appears as the spectral radius of a Perron-Frobenius matrix preserving a symplectic form LL. We investigate what algebraic units could potentially appear as dilatatio…

2011-04-13abs ↗pdf ↗

This paper extends transfer operator theory to McKean-Vlasov equations.

problem Analyzing the behavior of complex dynamical systems using transfer operators.
method Extended dynamic mode decomposition and Galerkin projection.
result Finite-dimensional approximations of transfer operators computed.

Study of endperiodic maps on infinite graphs, proving homotopy and eigenvalue properties.

problem Understanding endperiodic maps on infinite graphs with finitely many ends.
method Adapting relative train track maps and combinatorial techniques to infinite type setting.
result Any generalized endperiodic map is homotopic to a relative train track map.

The development of a metric for structural data is a long-term problem in pattern recognition and machine learning. In this paper, we develop a general metric for comparing nonlinear dynamical systems that is defined with Perron-Frobenius operators in reproducing kernel Hilbert spaces. Our metric includes the existing …

2018-05-31abs ↗pdf ↗

Asset prices contain information about the probability distribution of future states and the stochastic discounting of those states as used by investors. To better understand the challenge in distinguishing investors' beliefs from risk-adjusted discounting, we use Perron-Frobenius Theory to isolate a positive martingal…

2014-11-28abs ↗pdf ↗

Stochastic discount factor (SDF) processes in dynamic economies admit a permanent-transitory decomposition in which the permanent component characterizes pricing over long investment horizons. This paper introduces an empirical framework to analyze the permanent-transitory decomposition of SDF processes. Specifically, …

2014-12-15abs ↗pdf ↗

GradientDICE improves offline estimation for reinforcement learning policies.

problem Estimating density ratios between target policy and sampling distributions in reinforcement learning.
method GradientDICE reparameterizes the optimization problem to avoid nonlinearity, ensuring convergence and consistency.
result GradientDICE is provably convergent and eliminates the need for nonlinearity in parameterization.

In this paper we develop the metric theory for the outer space of a free product of groups. This generalizes the theory of the outer space of a free group, and includes its relative versions. The outer space of a free product is made of GG-trees with possibly non-trivial vertex stabilisers. The strategies are the same…

2013-12-15abs ↗pdf ↗

If (M,g)(M,g) is a compact real analytic Riemannian manifold, we give a necessary and sufficient condition for there to be a sequence of quasimodes of order o(λ)o(λ) saturating sup-norm estimates. In particular, it gives optimal conditions for existence of eigenfunctions satisfying maximal sup norm bounds. The condition is …

2013-11-15abs ↗pdf ↗

The paper estimates key metrics for linear models with Markov or hidden Markov sources.

problem Estimating free energy, mutual information, and MMSE for linear models with specific signal priors.
method Replica analysis in statistical physics, focusing on Markov and hidden Markov sources.
result The linear model with Markov or hidden Markov sources can be simplified into decoupled AWGN channels.

RaNNDy uses randomized neural networks to learn transfer operators efficiently.

problem Efficiently learning transfer operators from data.
method Randomized neural network approach with randomly initialized hidden layers and trained output layer.
result Significant reduction in training time and resources with improved stability.

Data-driven methods link graphon limits to random walks and spectral clustering.

problem Clustering signals evolving over time with graphon limits.
method Transfer operators, Koopman and Perron-Frobenius, for estimating graphon from signal data.
result Spectral clustering can be extended to graphons, reconstructing transition densities and graphons.

New guarantees for uniquely identifying transport maps and vector fields from finite measure-valued data.

problem Unique recovery of transport maps and vector fields from finite measure-valued data.
method Use of Whitney and Takens embedding theorems to establish conditions for unique identification.
result New metric for comparing diffeomorphisms and analogous results in infinitesimal settings.

The paper connects function theory, dynamics, and ergodic theory via Thurston's theory.

problem Function theory on Teichmüller space and dynamics of mapping class groups.
method Utilizes Thurston's theory and Sullivan's theory on discrete subgroups of hyperbolic space.
result Establishes connections between function theory, dynamics, and ergodic theory.

Lectures on topological field theories and differential cohomology.

problem Exploring topological field theories and their connections to differential cohomology.
method Introduction to topological field theory and generalized Abelian gauge theories.
result Explains the relationship between topological field theories and differential cohomology.

The paper defines strong emergence in field theories and proves it exists between certain theories.

problem Defining and proving the existence of strong emergence phenomena between field theories.
method Formal definition and sufficient conditions for emergence, proving existence in Euclidean background.
result Strong emergence exists between certain parameterized Lagrangian field theories.

Researchers find new G2G_2-conifolds in MM-theory with potential field theory duals.

problem Exploring the field theory interpretation of MM-theory G2G_2-conifolds.
method Constructing G2G_2-holonomy orbifolds from circle bundles over Calabi-Yau cones.
result Many UV perturbative gauge theories have an infrared dual described by smooth G2G_2-holonomy backgrounds in MM-theory.

We survey three different ways in which K-theory in all its forms enters quantum field theory. In Part 1 we give a general argument which relates topological field theory in codimension two with twisted K-theory, and we illustrate with some finite models. Part 2 is a review of pfaffians of Dirac operators, anomalies, a…

2002-06-18abs ↗pdf ↗

Distributivity in algebraic structures appeared in many contexts such as in quasigroup theory, semigroup theory and algebraic knot theory. In this paper we give a survey of distributivity in quasigroup theory and in quandle theory.

2012-09-28abs ↗pdf ↗

Main mathematical applications of Frobenius manifolds are in the theory of Gromov - Witten invariants, in singularity theory, in differential geometry of the orbit spaces of reflection groups and of their extensions, in the hamiltonian theory of integrable hierarchies. The theory of Frobenius manifolds establishes rema…

1998-07-08abs ↗pdf ↗

In this paper, we construct a new homology theory for semi-groups satisfying the self distributivity axiom or the idempotency axiom. Next, we consider the geometric realization corresponding to the homology theory. We continue with the comparison of this homology theory with one term and two term (rack) homology theori…

2016-11-17abs ↗pdf ↗

This thesis proposes a global geometric formulation of Extended Field Theories.

problem Global understanding of Extended Field Theories remains an open problem.
method Introducing an atlas for the principal infinity-bundle, unifying metric and higher gauge field.
result Global abelian T-duality and Poisson-Lie T-duality are automatically recovered.