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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.

169,051 papers · 148 categories

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92185277369 · Jun 202019922001200920172026
48 results for nonlinear derivatives

Develops a new approach to study nonlinear PDEs and their singularities.

problem Understanding the propagation domains of solutions to nonlinear PDEs.
method Derived geometric machinery and sheaf theory to study nonlinear PDEs and their singular supports.
result Estimates the domains of propagation for solutions of non-linear systems.

Proposes a new derivative concept for nonlinear DRO problems.

problem Optimizing nonlinear functions in probability space with distributionally robust optimization.
method Introduces Gateaux derivative for smoothness and proposes a Frank-Wolfe algorithm.
result Validates theoretical results on portfolio selection problems with numerical validation.

The paper derives new gradient and Hessian estimates for nonlinear parabolic equations.

problem Estimating solutions to nonlinear weighted parabolic equations.
method Derives Li-Yau and Hamilton type gradient estimates, and Hessian estimates.
result New gradient and Hessian estimates for positive solutions of nonlinear parabolic equations.

Method solves complex optimization problems with high probability bounds.

problem Nonlinear equality constrained stochastic optimization problems.
method Step-search sequential quadratic programming method.
result High-probability bound on iteration complexity for first-order stationarity.

The paper derives subgradient estimates for a specific nonlinear subparabolic equation on pseudo-Hermitian manifolds.

problem Deriving subgradient estimates for positive solutions to a nonlinear subparabolic equation on pseudo-Hermitian manifolds.
method Using the CR sub-Laplacian comparison property, the paper derives local subgradient estimates for positive solutions to the given equation.
result The paper establishes subgradient estimates for positive solutions to the nonlinear subparabolic equation.

Data-driven control of robotic systems using Koopman operators with error bounds.

problem Real-time control of nonlinear robotic systems with unknown dynamics.
method Constructing a Koopman operator-based linear representation using higher-order derivatives of nonlinear dynamics, with error bounds derived from Taylor series accuracy analysis.
result The Koopman model provides marginally better performance than competing nonlinear modeling methods and can be efficiently controlled using linear control design tools.

Study moduli spaces of elliptic PDEs using derived CC^{\infty}-geometry.

problem Representability of moduli spaces of solutions of elliptic PDEs.
method Derived CC^{\infty}-geometry, stacks of relative jets, nonlinear Fredholm analysis.
result Moduli stack of solutions is relatively representable by quasi-smooth derived CC^{\infty}-schemes.

We present a method to derive local estimates for some classes of fully nonlinear elliptic equations. The advantage of our method is that we derive Hessian estimates directly from C0C^0 estimates. Also, the method is flexible and can be applied to a large class of equations.

2005-10-29abs ↗pdf ↗

Paper uses IGA for efficient pricing of financial derivatives, comparing it to FDM and FEM.

problem Efficiently pricing complex financial derivatives with high accuracy.
method Isogeometric Analysis (IGA) for solving nonlinear Black-Scholes PDEs.
result IGA provides very accurate solutions with fewer knots, significantly reducing computational time.

We investigate structured sparsity methods for variable selection in regression problems where the target depends nonlinearly on the inputs. We focus on general nonlinear functions not limiting a priori the function space to additive models. We propose two new regularizers based on partial derivatives as nonlinear equi…

2018-05-16abs ↗pdf ↗

Stochastic VB improves nonlinear model inference speed and accuracy.

problem Bayesian inference of nonlinear models from noisy data.
method Stochastic Variational Bayesian (VB) inference for nonlinear models.
result Stochastic VB achieves comparable parameter recovery to analytical solution but is faster.

We show that, locally, all geometric objects of Generalized Kahler Geometry can be derived from a function K, the "generalized Kahler potential''. The metric g and two-form B are determined as nonlinear functions of second derivatives of K. These nonlinearities are shown to arise via a quotient construction from an aux…

2007-03-12abs ↗pdf ↗

Paper presents a new backward deep BSDE method for solving nonlinear FBSDE problems.

problem Nonlinear Forward Backward Stochastic Differential Equations (FBSDE) with terminal conditions.
method Backward deep BSDE method applied to FBSDE with nonlinear generators and random initial conditions.
result Derives exact and Taylor-based approximations for time-stepping nonlinear BSDEs.

Estimates for complex equations on manifolds derived from a conjecture.

problem Estimating solutions to complex equations on Hermitian manifolds.
method Developed second order estimates for fully nonlinear elliptic equations with gradient terms.
result Derived global estimates for an equation related to Gauduchon's conjecture.

The study sets limits on how well nonlinear models can generalize from training data.

problem Understanding the limits of generalization for nonlinear learning models.
method Deriving explicit generalization lower bounds for multi-layer neural networks and linear regression.
result Explicit bounds for general biased estimators in nonlinear networks, showing unacceptable performance for unbiased estimators.

Families of explicit solutions are found to a nonlinear Black-Scholes equation which incorporates the feedback-effect of a large trader in case of market illiquidity. The typical solution of these families will have a payoff which approximates a strangle. These solutions were used to test numerical schemes for solving …

2006-04-05abs ↗pdf ↗

This research highlights the secrecy potential of nonlinear generative models and their all-or-nothing phase transition.

problem Secrecy potential of nonlinear generative models in statistical learning.
method Replica method to derive asymptotic normalized cross entropy and statistical decoupling of Bayesian estimator.
result Strictly nonlinear models exhibit an all-or-nothing phase transition, leading to perfect secrecy.

Derives gradient estimate for a specific nonlinear parabolic equation on Finsler manifolds.

problem Derives gradient estimate for a nonlinear parabolic equation on Finsler manifolds.
method Leverages a new Laplacian comparison theorem to derive a Li-Yau type gradient estimate.
result Establishes a Li-Yau type gradient estimate for the Finslerian logarithmic Schrödinger equation.

Unified framework for selecting variables with uncertainty quantification.

problem Uncertainty in nonlinear variable selection for various models.
method Develops a unified framework using integrated partial derivatives for quantifying variable importance and uncertainty.
result The approach provides a principled method for quantifying variable selection uncertainty and is generalizable to non-differentiable models.

In this paper we study some geometrical objects (d-tensors, multi-time semisprays of polymomenta and nonlinear connections) on the dual 1-jet vector bundle J1(T,M)T×MJ^{1*}(\cal{T}, M)\to \cal{T}\times M. Some geometrical formulas, which connect the last two geometrical objects, are also derived. Finally, a canonical nonlinear…

2008-07-06abs ↗pdf ↗

The objective of this paper is to provide a comprehensive study no-arbitrage pricing of financial derivatives in the presence of funding costs, the counterparty credit risk and market frictions affecting the trading mechanism, such as collateralization and capital requirements. To achieve our goals, we extend in severa…

2017-01-29abs ↗pdf ↗

This paper develops a fast algorithm for solving nonlinear PDEs using sparse Cholesky factorization.

problem Efficiently solving nonlinear PDEs with Gaussian processes and kernel methods.
method Sparse Cholesky factorization for near-linear complexity.
result Near-linear complexity algorithm for working with kernel matrices of nonlinear PDEs.

The main purposes of this article are to extend our previous results on homogeneous sprays to arbitrary (generalized) sprays, to show that locally diffeomorphic exponential maps can be defined for any (generalized) spray, and to give a (possibly nonlinear) covariant derivative for any (possibly nonlinear) connection. I…

2003-04-04abs ↗pdf ↗

The paper extends Li-Yau-Hamilton estimates to evolving Kähler metrics and nonlinear heat equations.

problem Deriving estimates for nonlinear heat equations on evolving Kähler metrics.
method Generalized matrix Li-Yau-Hamilton estimates to Kähler manifolds with evolving metrics and nonlinear heat equations.
result Extended Li-Yau-Hamilton estimates to evolving Kähler metrics and nonlinear heat equations.

This work examines the stability of GD and SGD near minima, revealing nonlinear dynamics that differ from linear analysis.

problem The stability of optimization algorithms like GD and SGD near minima is not well understood.
method The authors derive an exact criterion for stable oscillations of GD near minima in the multivariate setting, considering high-order derivatives.
result Nonlinear dynamics can diverge in expectation even if a single batch is unstable, challenging linear analysis.

Solves a specific Dirichlet problem on Riemannian manifolds.

problem Dirichlet problem for degenerate fully nonlinear elliptic equations on Riemannian manifolds.
method Derives existence of C1,1C^{1,1}-solutions under appropriate assumptions.
result Existence of C1,1C^{1,1}-solutions.

The paper studies fully nonlinear equations on Hermitian manifolds, proving existence and interior estimates.

problem Proving existence and interior estimates for fully nonlinear equations on Hermitian manifolds.
method Derives interior estimates and establishes the existence of smooth solutions for the Dirichlet problem and equations on closed manifolds.
result Derives interior estimates and establishes the existence of smooth solutions for the Dirichlet problem and equations on closed manifolds.