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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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3774111148 · Jun 202019922001200920172026
48 results for discrete motion

We consider nn-dimensional discrete motions such that any two neighbouring positions correspond in a pure rotation ("rotating motions"). In the Study quadric model of Euclidean displacements these motions correspond to quadrilateral nets with edges contained in the Study quadric ("rotation nets"). The main focus of ou…

2010-04-08abs ↗pdf ↗

We construct explicit solutions to the discrete motion of discrete plane curves that has been introduced by one of the authors recently. Explicit formulas in terms the ττ function are presented. Transformation theory of the motions of both smooth and discrete curves is developed simultaneously.

2010-08-17abs ↗pdf ↗

Study on Brownian motion on discrete curve spaces, proving stochastic completeness.

problem Analyzing Brownian motion on spaces of discrete curves.
method Introduced and studied Brownian motion on spaces of discrete regular curves with Sobolev-type metrics.
result All geodesically complete spaces of discrete regular curves are stochastically complete.

We study the motion of discrete interfaces driven by ferromagnetic interactions in a two-dimensional periodic environment by coupling the minimizing movements approach by Almgren, Taylor and Wang and a discrete-to-continuous analysis. The case of a homogeneous environment has been recently treated by Braides, Gelli and…

2014-07-26abs ↗pdf ↗

We study the motion of discrete interfaces driven by ferromagnetic interactions on the two-dimensional triangular lattice by coupling the Almgren, Taylor and Wang minimizing movements approach and a discrete-to-continuum analysis, as introduced by Braides, Gelli and Novaga in the pioneering case of the square lattice. …

2018-06-30abs ↗pdf ↗

We study the motion of discrete interfaces driven by ferromagnetic interactions in a two-dimensional low-contrast periodic environment, by coupling the minimizing movements approach by Almgren, Taylor and Wang and a discrete-to-continuum analysis. As in a recent paper by Braides and Scilla dealing with high-contrast pe…

2014-07-25abs ↗pdf ↗

We study the geometry of the inextensible string (the whip) and its discrete approximation (the chain). In the absence of gravity, both motions represent geodesic motions on certain manifolds. We show how the motion of the chain converges to that of a whip, and how the curvature of the chain's configuration space conve…

2008-04-09abs ↗pdf ↗

Derives EoM for DNNs to describe GD dynamics precisely.

problem Gaps between differential equations and actual DNN learning dynamics due to discretization error.
method Starts from GF, derives counter term to cancel discretization error, obtains EoM.
result EoM precisely describes GD dynamics of DNNs, highlights differences between continuous and discrete GD.

Lyons and Sullivan have shown how to discretize harmonic functions on a Riemannian manifold MM whose Brownian motion satisfies a certain recurrence property called \ast-recurrence. We study analogues of this discretization for tensor fields which are harmonic in the sense of the covariant Laplacian. We show that, un…

2016-03-28abs ↗pdf ↗

Deep network predicts action sequences for complex tasks from a scene image.

problem Scalable task and motion planning from initial scene images.
method Deep convolutional recurrent neural network that predicts action sequences.
result Predicts promising action sequences, reducing motion planning problems.

Study evaluates discretized arbitrage strategies in fractional financial markets.

problem Serial correlation in financial markets with fractional Brownian motion.
method Revisit and transfer Shiryaev and Salopek's strategies to a real-world setting, distretizing dynamics and introducing transaction costs.
result Both strategies are promising with respect to terminal portfolio values and loss probabilities.

This work improves motion planning for quadcopters by learning and reasoning about controller performance.

problem Improving motion planning for quadcopters with safety margins and execution reliability.
method Introspective learning and reasoning to correct execution bias and improve collision checking.
result Substantial reduction in safety margins for motion actions, leading to safer execution.

This work compresses sequences by treating them as continuous-time processes, enabling efficient discretization.

problem Efficient compression of sequences, especially with deep learning models that scale with sequence length.
method Treat sequences as continuous-time processes, learn efficient discretization, and decode at different time intervals.
result Automatic bit rate reductions in video and motion capture sequences using learned discretization.

Study on error rates for approximating rough volatility models.

problem Simulation of rough volatility models with fractional Brownian motion.
method Analysis of weak error rates for numerical schemes, focusing on fBm and cubic test functions.
result Convergence rates for approximations are (3H+12)1(3H+ \frac{1}{2}) \wedge 1 for exact left-point discretization and H+12H+\frac{1}{2} for hybrid schemes.

Connections on principal bundles play a fundamental role in expressing the equations of motion for mechanical systems with symmetry in an intrinsic fashion. A discrete theory of connections on principal bundles is constructed by introducing the discrete analogue of the Atiyah sequence, with a connection corresponding t…

2005-08-18abs ↗pdf ↗

We develop variational integrators from discrete Hamiltonian systems with external forces.

problem Creating accurate discrete models of continuous Hamiltonian systems.
method Constructing discrete Hamiltonian systems with external forces, analyzing symplectic structure, and combining methods to build variational integrators.
result We derive variational integrators that approximate continuous Hamiltonian systems with high accuracy.

Estimates roughness of volatility from discrete variance data.

problem Estimating roughness exponent of stochastic volatility from discrete observations of integrated variance.
method Pathwise estimator based on fractional Brownian motion with drift.
result Strong consistency theorems for rough volatility models.

Bäcklund transformations for smooth and ``space discrete'' Hashimoto surfaces are discussed and a geometric interpretation is given. It is shown that the complex curvature of a discrete space curve evolves with the discrete nonlinear Schrödinger equation (NLSE) of Ablowitz and Ladik, when the curve evolves with the Has…

2000-07-25abs ↗pdf ↗

Continuous time models in the theory of real options give explicit formulas for optimal exercise strategies when options are simple and the price of an underlying asset follows a geometric Brownian motion. This paper suggests a general, computationally simple approach to real options in discrete time. Explicit formulas…

2004-04-05abs ↗pdf ↗

The paper introduces discrete Dirac structures for mechanics, simplifying dynamics.

problem Formulating discrete mechanics with constraints.
method Developed (±)(\pm)-discrete Dirac structures and induced Dirac structures.
result Discrete Lagrange--Dirac systems are equivalent to (±)(\pm)-discrete Lagrange--d'Alembert equations.

Paper extends a method to estimate Hurst parameter for rough stochastic volatility models.

problem Estimating Hurst parameter of rough stochastic volatility models from discrete observations.
method Extends a scale-invariant estimator to a general nonlinear function.
result Consistent estimation of Hurst parameter for a wide class of rough stochastic volatility models.

Skew parallelogram nets factorize, encompassing discrete differential geometry.

problem Factorization of polynomials in discrete differential geometry.
method Lax representation, Bäcklund transformations, factorization of polynomials.
result Skew parallelogram nets encompass all systems with polynomial representations.

Innovative extensions to option pricing models using asymmetric Brownian motion and random walk approaches.

problem Capturing empirical phenomena like return skewness, heavy tails, and volatility asymmetry in option pricing models.
method Developing the Geometric Asymmetric Brownian Motion (GABM) within the Bachelier--Black--Scholes--Merton framework.
result Deriving closed-form option pricing formulas and a discrete-time binomial tree algorithm that converges to the GABM limit.

In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approache…

2018-10-30abs ↗pdf ↗

Extended Regge complex for linearized Riemann-Cartan geometry and cohomology.

problem Cohomology of the Regge complex in three dimensions.
method Constructing a discrete version of linearized Riemann-Cartan geometry on any triangulation.
result The cohomology of the Regge complex is isomorphic to the infinitesimal-rigid-body-motion-valued de~Rham cohomology.

Model rough volatility using RDEs with correlated Brownian motion and fractional Brownian motion.

problem Modeling rough volatility with correlated stochastic processes.
method Developed a method to lift Brownian motion and rough paths, applying it to fractional Brownian motion to model rough volatility.
result Calibrated a new rough volatility model to market data.

New perspective on SGD reveals short-range memory effects in deep learning.

problem Understanding the efficacy of stochastic gradient descent (SGD) in deep learning.
method Proposed that SGD is a discretization of an SDE driven by fractional Brownian motion (FBM).
result SGD stays longer in flat minima, favoring generalization.

A single-vertex origami is a piece of paper with straight-line rays called creases emanating from a fold vertex placed in its interior or on its boundary. The Single-Vertex Origami Flattening problem asks whether it is always possible to reconfigure the creased paper from any configuration compatible with the metric, t…

2010-03-17abs ↗pdf ↗

Optimal strategy for liquidating portfolios under discrete time intervals.

problem Optimizing liquidation of portfolios with discrete time constraints and impact effects.
method Modeling portfolio liquidation with N risky assets, using VaR for cost measurement, and deriving an optimal liquidation time.
result The optimal liquidation time is only influenced by temporary price impacts, not permanent ones.

This note clarifies connections between Föllmer process and DDPM sampler.

problem Understanding the relationship between Föllmer process and DDPM sampler.
method Direct discretization of the Föllmer process and DDPM sampler analysis.
result Discretized Föllmer processes provide optimal hyper-parameters for DDPM samplers.

This paper surveys DRL for autonomous vehicle motion planning.

problem Designing intelligent motion planning for autonomous vehicles.
method Deep Reinforcement Learning (DRL) for hierarchical motion planning.
result Survey of state-of-the-art DRL solutions for autonomous vehicle motion planning.

ES reduces high-probability regret in stochastic linear bandits.

problem High-probability regret in stochastic linear bandits.
method Linear ensemble sampling with standard Gaussian perturbations, analyzing m=Θ(dlogn)m=Θ(d\log n) ensemble size.
result ES achieves ildeO(d3/2n) ilde O(d^{3/2}\sqrt n) high-probability regret, closing the gap to Thompson sampling.