Research
On-device research index

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.

168,657 papers · 148 categories

Trend · papers per month

76152228304 · Jun 202019922001200920172026
48 results for homogeneous dynamics

Study on Ricci flows of awesome homogeneous spaces, proving finite extinction time.

problem Understanding the long-time behavior of Ricci flows on homogeneous spaces.
method Analyzing Ricci flows on non-compact manifolds, focusing on finite extinction time.
result Ricci flows on non-contractible spaces have finite extinction time, confirming conjecture.

The paper analyzes neural network dynamics after weights escape the origin.

problem Understanding gradient flow dynamics of neural networks after the origin.
method Analyzes gradient flow of homogeneous neural networks with locally Lipschitz gradients.
result Characterizes the first saddle point encountered after escaping the origin.

Steerable neural ODEs on homogeneous spaces for equivariant feature dynamics.

problem Learning continuous-time equivariant dynamics of vector-valued features on homogeneous spaces.
method Introduces steerable neural ordinary differential equations on homogeneous spaces, interpreting features as sections of associated vector bundles over MM.
result Steerable NODEs are GG-equivariant when the flow and connection are GG-invariant, and they incorporate existing models.

Study on deep multi-head self-attention dynamics, proving homogenized limits under specific scalings.

problem Understanding the behavior of deep multi-head self-attention models as depth increases.
method Random model of deep multi-head self-attention, viewing depth as time, and analyzing the residual stream as a particle system.
result Homogenized limit of the dynamics, leading to deterministic or stochastic behavior depending on scaling, with implications for representation collapse.

The dynamics defined by a force field which is positively homogeneous of degree 3-3 can always be reduced, by simply constraining it. The dimension of the phase space is reduced by two dimensions, while it may only be reduced by one dimension if the degree of homogeneity is different from 3-3. This remark is an elega…

2014-12-12abs ↗pdf ↗

The paper simplifies multi-agent RL dynamics in finite-state Markov games using homogenization.

problem Approximating complex multi-agent reinforcement learning dynamics in finite-state Markov games.
method Rescaling learning process by reducing learning rate and increasing update frequency, proving convergence to an ODE.
result The rescaled process converges to an ODE that approximates the agent's learning dynamics.

A new microeconomic model is presented that aims at a description of the long-term unit sales and price evolution of homogeneous non-durable goods in polypoly markets. It merges the product lifecycle approach with the price dispersion dynamics of homogeneous goods. The model predicts a minimum critical lifetime of non-…

2011-09-27abs ↗pdf ↗

We consider the Ricci flow equation for invariant metrics on compact and connected homogeneous spaces whose isotropy representation decomposes into two irreducible inequivalent summands. By studying the corresponding dynamical system, we completely describe the behaviour of the homogeneous Ricci flow on this kind of sp…

2012-09-13abs ↗pdf ↗

We study the evolution of homogeneous Ricci solitons under the bracket flow, a dynamical system on the space of all homogeneous spaces of dimension n with a q-dimensional isotropy, which is equivalent to the Ricci flow for homogeneous manifolds. We prove that algebraic solitons (i.e. the Ricci operator is a multiple of…

2012-10-12abs ↗pdf ↗

SGD converges to critical points of normalized margin in late-stage training for homogeneous neural networks.

problem Analyzing the implicit bias of SGD on homogeneous neural networks.
method Interpreting SGD dynamics as an Euler-like discretization of a conservative field flow associated with the normalized classification margin.
result Normalized SGD iterates converge to the set of critical points of the normalized margin at late-stage training.

The paper defines unimodularity for coisotropic Poisson spaces and discusses invariant volume forms.

problem Understanding unimodularity and invariant volume forms for Hamiltonian dynamics on coisotropic Poisson spaces.
method Introducing multiplicative unimodularity and discussing its properties for coisotropic Poisson homogeneous spaces.
result Existence of invariant volume forms for explicit Hamiltonian systems on coisotropic Poisson spaces.

The paper studies neural networks' convergence near origin and saddle points.

problem Directional convergence of neural networks near small initializations and saddle points.
method Gradient flow dynamics analysis of two-homogeneous neural networks.
result Neural networks' weights approximately converge in direction to KKT points for small initializations.

We survey the use of dynamics of SL(2,R)SL(2, \R)-actions to understand gap distributions for various sequences of subsets of [0,1)[0, 1), particularly those arising from special trajectories of various two-dimensional dynamical systems. We state and prove an abstract theorem that gives a unified explanation for some of the ex…

2012-10-02abs ↗pdf ↗

We embed KKT points in neural networks of different sizes.

problem Classifying data using homogeneous neural networks.
method Introducing KKT point embedding principle and proving it for different network types.
result KKT points of a smaller network can be mapped to those of a larger network via linear transformations.

The paper shows measures equidistribute on affine submanifolds with a rate.

problem Understanding equidistribution of measures on affine invariant submanifolds.
method Analyzing unstable foliations and using results from homogeneous dynamics.
result Measures of large dimension equidistribute on affine invariant submanifolds with an effective rate.

This work analyzes the maximum-margin bias in quasi-homogeneous neural networks.

problem Analyzing the maximum-margin bias in quasi-homogeneous neural networks.
method Geometric analysis of gradient dynamics for quasi-homogeneous models.
result Gradient flow implicitly favors a subset of parameters, leading to asymmetric norm minimization.

In this paper we prove that a wild knot KK which is the limit set of a Kleinian group acting conformally on the unit 3-sphere, with its standard metric, is homogeneous: given two points p,qKp, q\in{K} there exists a homeomorphism ff of the sphere such that f(K)=Kf(K)=K and f(p)=qf(p)=q. We also show that if the wild knot is a …

2005-08-26abs ↗pdf ↗

The paper characterizes strong Hamel functions using symmetries and proves their preservation properties.

problem Characterizing strong Hamel functions and their symmetries in Finsler spaces.
method Analyzing geodesic spray, strong dual symmetries, and strong dynamical symmetries.
result Strong Hamel functions can be characterized in terms of strong dual symmetries and strong dynamical symmetries.

The application of the Legendre transformation to a hyperregular Lagrangian system results in a Hamiltonian vector field generated by a Hamiltonian defined on the phase space of the mechanical system. The Legendre transformation in its usual interpretation can not be applied to homogeneous Lagrangians found in relativi…

2004-04-29abs ↗pdf ↗

This short expository note gives an elementary introduction to the study of dynamics on certain moduli spaces, and in particular the recent breakthrough result of Eskin, Mirzakhani, and Mohammadi. We also discuss the context and applications of this result, and connections to other areas of mathematics such as algebrai…

2015-04-30abs ↗pdf ↗

Study investigates Einstein flow stability and convergence with matter sources.

problem Stability and convergence of Einstein flow with matter sources.
method Incorporates matter sources into the Einstein flow and examines stability and convergence.
result Similar conclusions can be drawn about the evolution of manifolds to approximate homogeneity and isotropy.

Mirzakhani's thesis counts geodesics on hyperbolic surfaces, finding a specific asymptotic formula.

problem Counting simple closed geodesics on hyperbolic surfaces.
method Inspired by lattice point counting, uses principles of homogeneous dynamics.
result The number of simple closed geodesics of length ≤ L is asymptotic to L^(6g-6) times a constant.

Study shows metrics on certain manifolds lose positive curvature under Ricci flow.

problem Understanding the dynamics of positively curved metrics on specific manifolds.
method Analysis of invariant metrics on SU(3)/T2\mathrm{SU}(3)/\mathrm{T}^2 and SU(m+2p)/S(U(m)imesU(p)imesU(p))\mathrm{SU}(m+2p)/\mathrm{S}(\mathrm{U}(m) imes\mathrm{U}(p) imes \mathrm{U}(p)) under homogeneous Ricci flow.
result Metrics lose positive intermediate Ricci curvature under Ricci flow for certain dimensions.

We show that three-dimensional homogeneous Ricci flow solutions that admit finite-volume quotients have long-time limits given by expanding solitons. We show that the same is true for a large class of four-dimensional homogeneous solutions. We give an extension of Hamilton's compactness theorem that does not assume a l…

2005-09-27abs ↗pdf ↗

A variational equation of the fourth order for the free relativistic top is developed starting from the Dixon's system of equations for the motion of the relativistic dipole. The obtained equation is then cast into the homogeneous space-time Hamiltonian form.

2015-10-21abs ↗pdf ↗

We consider the classical Merton problem of lifetime consumption-portfolio optimization problem with small proportional transaction costs. The first order term in the asymptotic expansion is explicitly calculated through a singular ergodic control problem which can be solved in closed form in the one-dimensional case. …

2012-02-28abs ↗pdf ↗

Early training of deep neural networks leads to small, directionally converging weights.

problem Training dynamics of deep homogeneous neural networks with small initializations.
method Gradient flow analysis and study of KKT points for neural correlation function.
result Weights converge in direction to KKT points during early training stages.

New integrators preserve geometric structure in Hamiltonian systems.

problem Preserving geometric structure in Hamiltonian systems on Jacobi manifolds.
method Combining Poissonization and symplectic bi-realizations to construct structure-preserving integrators.
result Explicit construction and application of Jacobi Hamiltonian integrators.

Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.

problem Understanding how heterogeneity impacts financial market dynamics.
method Agent-based model incorporating heterogeneous investment strategies and risk tolerance.
result Heterogeneity in strategies and risk tolerance suppresses price fluctuations.