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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.

168,695 papers · 148 categories

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48 results for iterated integrals

We deduce from the work of Chen, that the restriction morphism from closed free iterated integrals to closed iterated integrals on loops is onto. We use this to show that the module of higher order invariants of smooth functions is generated by free closed iterated integrals.

2010-11-15abs ↗pdf ↗

We give a definition of higher dimensional iterated integrals based on integration over membranes. We prove basic properties of this definition and formulate a conjecture which extends Chen's de Rham Theorem for iterated integrals to the membrane case.

2012-03-16abs ↗pdf ↗

Basic elements of integral calculus over algebras of iterated differential forms, are presented. In particular, defining complexes for modules of integral forms are described and the corresponding berezinians and complexes of integral forms are computed. Various applications and the integral calculus over the algebra $…

2006-10-30abs ↗pdf ↗

Analyzes string topology operations using Chen's integrals and homotopy transfer.

problem Relating string topology to perturbative Chern-Simons theory.
method Develops integrals over configuration spaces and applies homotopy transfer.
result Intertwines involutive Lie bialgebra structures on homology.

Analyzes L2L^{2}-harmonic forms on curved manifolds, proving integrability conditions.

problem Analyzing integrability of L2L^{2}-harmonic forms on curved manifolds.
method Established LL^{\infty}-estimate via Moser iteration, proved vanishing of integrable forms.
result Proves that L2L^{2}-harmonic forms on non-positively curved manifolds are integrable if and only if they vanish.

We construct a parallel transport on higher loop spaces of a manifold in term of a higher dimensional generalization of iterated path integrals. Under mild assumptions, we define a de Rham complex on higher loop spaces and we recover a known result of Hain of a de Rham structure on higher homotopy groups of a manifold.…

2012-06-25abs ↗pdf ↗

The paper derives new gradient estimates for nonlinear elliptic equations under integral Ricci curvature bounds.

problem Gradient estimates for nonlinear elliptic equations under integral Ricci curvature bounds.
method Moser's iteration method applied to positive solutions.
result New local and global gradient estimates for positive solutions are derived.

WaveFit uses fixed-point iteration to create high-quality neural vocoders.

problem Creating high-quality neural vocoders with fast inference.
method Integrates GANs' adversarial training into a DDPM-like iterative framework based on fixed-point iteration.
result WaveFit synthesizes speech with naturalness comparable to human speech, and is significantly faster than existing methods.

We introduce a novel numerical approach for a class of stochastic dynamic programs which arise as discretizations of backward stochastic differential equations or semi-linear partial differential equations. Solving such dynamic programs numerically requires the approximation of nested conditional expectations, i.e., it…

2016-05-24abs ↗pdf ↗

We give a formula of the Upsilon invariant of any L-space cable knot Kp,qK_{p,q} using p,ΥKp,Υ_K and ΥTp,qΥ_{T_{p,q}}. The integral value of the Upsilon invariant gives a Q{\mathbb Q}-valued knot concordance invariant. We compute the integral values for L-space iterated cable knots.

2017-03-26abs ↗pdf ↗

We construct bi-invariant total orderings of residually torsion-free nilpotent groups by using Chen's iterated integrals. This construction can be seen as a generalization of the Magnus ordering of the free groups, and equivalent to the classical construction which uses an iteration of central extensions. Our geometric…

2010-05-21abs ↗pdf ↗

Study of spaces of pure braids and string links using diagrams and integrals.

problem Understanding spaces of pure braids and string links through algebraic structures.
method Use of Kontsevich's CDGA of diagrams and Chen's iterated integrals to establish Hopf algebra isomorphisms and connections.
result Established a correspondence between Milnor invariants and Chen integrals for Brunnian spherical links.

Within a statistical learning setting, we propose and study an iterative regularization algorithm for least squares defined by an incremental gradient method. In particular, we show that, if all other parameters are fixed a priori, the number of passes over the data (epochs) acts as a regularization parameter, and prov…

2014-04-30abs ↗pdf ↗

This paper is a continuation of Akahori-Barsotti-Imamura (2017) and where the authors i) showed that a payment at a random time, which we call timing risk, is decomposed into an integral of static positions of knock-in type barrier options, ii) proposed an iteration of static hedge of a timing risk by regarding the hed…

2018-01-12abs ↗pdf ↗

Researchers find the optimal exercise time for American options using a specific type of diffusion process.

problem Finding the optimal time to exercise American options with a time-dependent Ornstein-Uhlenbeck process.
method Optimal stopping problem, probabilistic arguments, non-linear Volterra-type integral equation, Picard iteration algorithm.
result They derive a non-linear Volterra-type integral equation and prove the exercise boundary's Lipschitz continuity and differentiability almost everywhere.

Quantum speedup for Monte Carlo integration reduces integrand calls.

problem Reducing the number of calls to the integrand subroutine in high-dimensional Monte Carlo integration.
method Combining nested quantum amplitude estimation with pseudorandom numbers for separable integrands.
result Significant reduction in the number of integrand calls for high-dimensional integration.

The paper uses Fourier integral theorem for estimating multivariate distributions.

problem Estimating multivariate distributions and conditional distribution functions.
method Natural Monte Carlo and fully nonparametric estimators based on Fourier integral theorem.
result Explicit Monte Carlo estimators without estimated covariance matrix.

Let XX be a closed hyperbolic surface and λ,ηλ, η be weighted geodesic multicurves which are short on X. We show that the iterated grafting along λλ and ηη is close in the Teichmueller metric to grafting along a single multicurve which can be given explicitly in terms of λλ and ηη. Using this result, we study the h…

2008-02-22abs ↗pdf ↗

We prove that Wilson loop expectation values for arbitrary simple closed contours obey an area law up to second order in perturbative two-dimensional Yang-Mills theory. Our analysis occurs within a general family of axial-like gauges, which include and interpolate between holomorphic gauge and the Wu-Mandelstam-Liebran…

2016-01-18abs ↗pdf ↗

Improved neural network reconstruction from sparse measurements with theoretical guarantees.

problem Improving neural network performance in sparse signal reconstruction from few measurements.
method Combining iterative reconstruction algorithms with neural networks, analyzing generalization properties, and deriving a generalization bound.
result Theoretical guarantees for neural network reconstruction from compressive linear measurements, with generalization error scaling logarithmically in the number of layers and linearly in the number of measurements.

Proposes an INLA-based method for state and parameter estimation in nonlinear systems.

problem Difficulty in learning parameters accurately in nonlinear dynamical systems.
method Iterated INLA for state and parameter estimation in nonlinear dynamical systems.
result Outperforms existing methods on data assimilation tasks.

Paper tightens privacy and generalization bounds for iterative learning.

problem Balancing privacy and generalization in iterative learning algorithms.
method Established alignment between generalization and privacy, derived composition theorems for iterative algorithms.
result Generalization bounds for iterative learning algorithms are strictly tighter than existing works.

Neural networks enjoy widespread use, but many aspects of their training, representation, and operation are poorly understood. In particular, our view into the training process is limited, with a single scalar loss being the most common viewport into this high-dimensional, dynamic process. We propose a new window into …

2019-09-03abs ↗pdf ↗