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arXiv research

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.

168,742 papers · 148 categories

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125250374499 · Jun 202019922001200920172026
48 results for Lyapunov Spectral Analysis

Lyapunov analysis improves RNN performance prediction.

problem Uncertainty in RNN performance prediction due to hyperparameters and architecture.
method Lyapunov spectral analysis of RNNs and Autoencoder-Lyapunov Embedding Learning (AeLLE).
result AeLLE successfully correlates RNN Lyapunov spectrum with accuracy and predicts performance.

Study approximates top Lyapunov exponents for surface mapping classes.

problem Approximating topological Lyapunov exponents for surface mapping classes.
method Periodic approximation and joint spectral radius extension.
result Top Lyapunov exponents can be approximated by periodic orbits.

This work uses Lyapunov theory to improve the robustness of deep neural networks against adversarial attacks.

problem Vulnerability of deep neural networks to subtle adversarial perturbations.
method Treated each layer as a nonlinear dynamical system and used Lyapunov theory for stability and robustness.
result Developed empirically tight bounds on adversarial perturbations and proved stability and robustness globally.

Lyapunov's second theorem is an essential tool for stability analysis of differential equations. The paper provides an analog theorem for incremental stability analysis by lifting the Lyapunov function to the tangent bundle. The Lyapunov function endows the state-space with a Finsler structure. Incremental stability is…

2012-08-14abs ↗pdf ↗

On a smooth, compact and oriented manifold without boundary, we give a complete description of the correlation function of a Morse-Smale gradient flow satisfying a certain nonresonance assumption. This is done by analyzing precisely the spectrum of the generator of such a flow acting on certain anisotropic spaces of cu…

2016-05-18abs ↗pdf ↗

This expository paper, based on a Current Events Bulletin talk at the January, 2016 Joint Meetings, introduces the concept of Lyapunov exponents and discusses the role they play in three areas: smooth ergodic theory, Teichmüller theory, and the spectral theory of one-frequency Schrödinger operators. The inspiration for…

2016-08-09abs ↗pdf ↗

Study on convergence of SDEs using entropy methods.

problem Analyzing convergence of stochastic differential equations.
method Applied Lyapunov method to Fokker-Planck equation with weighted relative Fisher information.
result Exponential convergence of probability density function to invariant distribution in L1L_1 distance.

Unified analysis of stochastic iterative algorithms using Lyapunov functions.

problem Analyzing convergence of stochastic iterative algorithms for fixed-point equations.
method Lyapunov-based techniques for finite-time analysis of stochastic approximation algorithms.
result Unified mean-square convergence guarantees for various algorithms.

New method stabilizes deep neural networks by setting Lyapunov exponent to zero.

problem Stability issues in deep neural networks with low width.
method Lyapunov initialization method to set Lyapunov exponent to zero.
result Lyapunov exponent governs stability of deep networks; standard methods fail for low width.

New analysis improves SGD for robust and quantile regression with sub-quadratic convergence.

problem Improving SGD for robust and quantile regression with sub-quadratic convergence.
method Piecewise Lyapunov function for first-order differentiable functions.
result First geometrical convergence result for sub-quadratic SGD.

We consider vector fields XX on a closed manifold MM with rest points of Morse type. For such vector fields we define the property of exponential growth. A cohomology class ξH1(M;R)ξ\in H^1(M;\mathbb R) which is Lyapunov for XX defines counting functions for isolated instantons and closed trajectories. If XX has exponent…

2004-05-03abs ↗pdf ↗

Unified framework for finite-sample RL algorithms using Lyapunov theory.

problem Finite-sample convergence guarantees of asynchronous RL algorithms.
method Reformulate RL algorithms as Markovian SA, develop Lyapunov analysis.
result Mean-square error bounds and convergence for various RL algorithms.

New insights into using momentum for non-convex optimization.

problem Improving training of non-convex models like deep neural networks.
method Developed a Lyapunov analysis of SGD with momentum using stochastic primal averaging.
result Precise conditions under which SGD+M outperforms SGD and optimal hyper-parameter schedules.

New methods accelerate gradient descent for convex and strongly convex functions.

problem Improving convergence rates of gradient-based optimization methods.
method Formulated two classes of first-order algorithms with Lyapunov analyses and Hamiltonian assisted gradient method.
result Achieved accelerated convergence rates matching Nesterov's methods in strongly and general convex settings.

Optimal algorithms for Riemannian optimization with reduced complexity.

problem Stochastic optimization on Riemannian manifolds with limited data.
method Zeroth-order Riemannian Averaging Stochastic Approximation algorithms using Riemannian moving-average estimators and novel geometric conditions.
result Achieves optimal sample complexities for generating approximate first-order stationary solutions.

New approach to concentration inequalities for unbounded state space dynamical systems.

problem Concentration inequalities for unbounded state space dynamical systems.
method Functional analytic framework, transport-entropy inequality.
result Exponential concentration inequalities for sampling from stationary distribution.

Refines geometric center of mass analysis for Einstein field equations.

problem Analyzing the geometric center of mass of Willmore surfaces in initial data for Einstein field equations.
method Refined Lyapunov-Schmidt analysis to study geometric center of mass of area-constrained Willmore surfaces.
result The geometric center of mass agrees with the Hamiltonian center of mass under specific conditions.

The paper guarantees global stability for stochastic subgradient methods in nonsmooth nonconvex optimization.

problem Minimizing nonsmooth nonconvex functions with convergence guarantees.
method Developed a framework for stochastic subgradient methods with global stability guarantees.
result Iterates are uniformly bounded and asymptotically stabilize around the stable set of the differential inclusion.

We study the asymptotic behavior of the Lyapunov exponent in a meromorphic family of random products of matrices in SL(2, C), as the parameter converges to a pole. We show that the blow-up of the Lyapunov exponent is governed by a quantity which can be interpreted as the non-Archimedean Lyapunov exponent of the family.…

2018-03-20abs ↗pdf ↗

Policy gradient algorithm with variable learning rates achieves near-optimal performance in multi-arm bandit problems.

problem Optimizing a policy gradient algorithm for multi-arm bandit problems with variable learning rates.
method Applied Foster-Lyapunov techniques to analyze a Markov chain formed by the state of the algorithm.
result The policy gradient algorithm converges to the optimal arm with logarithmic or poly-logarithmic regret.

Operator calculus for population-based optimization provides a unified framework for analyzing convergence of various methods.

problem Convergence analysis of population-based optimization methods
method Introduce an operator calculus for describing composite mean-field algorithms as compositions of elementary operators acting on probability measures.
result Establish a modular Lyapunov principle for certifying exponential decay of state-space Lyapunov function and search errors.

Double Q-learning has the same mean-squared error as Q-learning under certain conditions.

problem Comparing the mean-squared error of Double Q-learning and Q-learning.
method Theoretical analysis based on Lyapunov equations for both tabular and linear function approximation settings.
result The asymptotic mean-squared error of Double Q-learning is exactly equal to that of Q-learning under specific conditions.

We study the relationship between the Lyapunov exponents of the geodesic flow of a closed negatively curved manifold and the geometry of the manifold. We show that if each periodic orbit of the geodesic flow has exactly one Lyapunov exponent on the unstable bundle then the manifold has constant negative curvature. We a…

2015-01-24abs ↗pdf ↗

In previous work, the author fully classified orbit closures in genus three with maximally many (four) zero Lyapunov exponents of the Kontsevich-Zorich cocycle. In this paper, we prove that there are no higher dimensional orbit closures in genus three with any zero Lyapunov exponents. Furthermore, if a Teichmüller curv…

2014-09-18abs ↗pdf ↗

The paper analyzes deep neural networks using control theory to set a time limit for their convergence.

problem Understanding the finite-time convergence of deep neural networks.
method Lyapunov based analysis of the loss function, control theory framework, finite-time control of non-linear systems.
result A priori guarantees of finite-time convergence for deep neural networks are provided.

The paper proves conditions for non-uniform expansion in partially hyperbolic systems.

problem Conditions for non-uniform expansion in partially hyperbolic systems.
method Analysis of Lyapunov exponents and dominated splittings.
result Existence of physical SRB measure under specific conditions.

Paper analyzes SA for fixed-point equations with noise, establishing convergence rates.

problem Solving fixed-point equations with noisy data.
method Uses smooth convex envelopes to construct Lyapunov functions and show negative drift.
result Establishes first-known convergence rate for V-trace algorithm in RL.

We consider actions of Z^k, k \ge 2, by Anosov diffeomorphisms which are uniformly quasiconformal on each coarse Lyapunov distribution. These actions generalize Cartan actions for which coarse Lyapunov distributions are one-dimensional. We show that, under certain non-resonance assumptions on the Lyapunov exponents, a …

2006-08-23abs ↗pdf ↗

Classifies GL(2,R)-invariant subvarieties with zero Lyapunov exponents.

problem Classifying GL(2,R)-invariant subvarieties with specific properties.
method Classification based on homological dimensions and Lyapunov exponents.
result Explicit exceptions list for GL(2,R)-invariant subvarieties with zero Lyapunov exponents.

Proves simplicity of Lyapunov exponents for specific Anosov flows.

problem Proving all Lyapunov exponents have multiplicity 1 for certain Anosov flows.
method Perturbative results for flows, modification of eigenvalues, Markov partition, and simplicity criterion.
result In a C1C^1-open and CkC^k-dense set of Anosov flows, all Lyapunov exponents have multiplicity 1.

Study of deep neural networks using finite-time Lyapunov exponents.

problem Understanding the geometric structures in input space formed by deep neural networks.
method Analogy with dynamical systems, computing finite-time Lyapunov exponents.
result Ridges of large positive exponents divide input space into regions associated with different classes.

Consider a family of K3 surfaces over a hyperbolic curve (i.e. Riemann surface). Their second cohomology groups form a local system, and we show that its top Lyapunov exponent is a rational number. One proof uses the Kuga-Satake construction, which reduces the question to Hodge structures of weight 1. A second proof us…

2014-12-04abs ↗pdf ↗

Stabilizes complex systems using diffusion models trained on Lyapunov functions.

problem Generating stabilizing controllers for complex dynamical systems.
method Trains a diffusion model on pairs of asymptotically stable vector fields and their Lyapunov functions to identify the closest stable field and adjust control functions.
result Efficient and rapid stabilization of unseen systems, showcasing generalizability.

We consider Lyapunov exponents for flat bundles over hyperbolic curves defined via parallel transport over the geodesic flow. We refine a lower bound obtained by Eskin, Kontsevich, Moeller and Zorich showing that the sum of the first k exponents is greater or equal than the sum of the degree of any rank k holomorphic s…

2018-10-30abs ↗pdf ↗