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In this manuscript we analyze the weak convergence rate of a discretization scheme for the Heston model. Under mild assumptions on the smoothness of the payoff and on the Feller index of the volatility process, respectively, we establish a…

Numerical Analysis · Mathematics 2016-04-20 Martin Altmayer , Andreas Neuenkirch

We introduce stabilized spline collocation schemes for the numerical solution of nonlinear, hyperbolic conservation laws. A nonlinear, residual-based viscosity stabilization is combined with a projection stabilization-inspired linear…

Numerical Analysis · Mathematics 2023-07-18 Ryan M. Aronson , John A. Evans

Splitting methods are widely used for solving initial value problems (IVPs) due to their ability to simplify complicated evolutions into more manageable subproblems which can be solved efficiently and accurately. Traditionally, these…

Numerical Analysis · Mathematics 2024-11-15 L. M. Kreusser , H. E. Lockyer , E. H. Müller , P. Singh

We explore a reinforcement learning strategy to automate and accelerate h/p-multigrid methods in high-order solvers. Multigrid methods are very efficient but require fine-tuning of numerical parameters, such as the number of smoothing…

Machine Learning · Computer Science 2024-07-24 David Huergo , Laura Alonso , Saumitra Joshi , Adrian Juanicoteca , Gonzalo Rubio , Esteban Ferrer

In this paper we investigate a new class of implicit-explicit (IMEX) two-step methods of Peer type for systems of ordinary differential equations with both non-stiff and stiff parts included in the source term. An extrapolation approach…

Numerical Analysis · Mathematics 2017-03-29 Jens Lang , Willem Hundsdorfer

In this paper, nonstandard multistep methods are considered. It is shown that under some (sufficient and necessary) conditions, these methods attain the same order as their standard counterparts - to prove this statement, a nonstandard…

Numerical Analysis · Mathematics 2026-01-19 Bálint Takács

In this paper we construct a third order method for solving additively split autonomous stiff systems of ordinary differential equations. The constructed additive method is L-stable with respect to the implicit part and allows to use an…

Numerical Analysis · Mathematics 2009-02-24 Evgeny Novikov , Anton Tuzov

The hybrid-high order (HHO) scheme has many successful applications including linear elasticity as the first step towards computational solid mechanics. The striking advantage is the simplicity among other higher-order nonconforming schemes…

Numerical Analysis · Mathematics 2026-04-10 Carsten Carstensen , Ngoc Tien Tran

In this paper, a novel stochastic extra-step quasi-Newton method is developed to solve a class of nonsmooth nonconvex composite optimization problems. We assume that the gradient of the smooth part of the objective function can only be…

Optimization and Control · Mathematics 2019-10-22 Minghan Yang , Andre Milzarek , Zaiwen Wen , Tong Zhang

We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms [Beck and Teboulle, 2009, Scheinberg et al., 2014] for composite optimization without the assumption that stochastic gradient is an unbiased…

Optimization and Control · Mathematics 2025-02-14 Lam M. Nguyen , Katya Scheinberg , Trang H. Tran

We introduce a novel multi-factor Heston-based stochastic volatility model, which is able to reproduce consistently typical multi-dimensional FX vanilla markets, while retaining the (semi)-analytical tractability typical of affine models…

Pricing of Securities · Quantitative Finance 2015-03-20 Alvise De Col , Alessandro Gnoatto , Martino Grasselli

We propose a new, data-driven approach for efficient pricing of - fixed- and float-strike - discrete arithmetic Asian and Lookback options when the underlying process is driven by the Heston model dynamics. The method proposed in this…

Computational Finance · Quantitative Finance 2024-02-19 Leonardo Perotti , Lech A. Grzelak

We develop and analyze a stabilization term for cut finite element approximations of an elliptic second order partial differential equation on a surface embedded in $\mathbb{R}^d$. The new stabilization term combines properly scaled normal…

Numerical Analysis · Mathematics 2018-08-23 Mats G. Larson , Sara Zahedi

We derive normal approximation results for a class of stabilizing functionals of binomial or Poisson point process, that are not necessarily expressible as sums of certain score functions. Our approach is based on a flexible notion of the…

Probability · Mathematics 2022-10-20 Zhaoyang Shi , Krishnakumar Balasubramanian , Wolfgang Polonik

Stability selection is a widely adopted resampling-based framework for high-dimensional variable selection. This paper seeks to broaden the use of an established stability estimator to evaluate the overall stability of the stability…

Methodology · Statistics 2025-06-04 Mahdi Nouraie , Samuel Muller

We discuss systematic extensions of the standard (St{\"o}rmer-Verlet) splitting method for differential equations of Hamiltonian mechanics, with relative accuracy of order $\tau^2$ for a timestep of length $\tau$, to higher orders in…

Numerical Analysis · Mathematics 2013-10-09 Asif Mushtaq , Anne Kværnø , Kåre Olaussen

We study step-wise time approximations of non-linear hyperbolic initial value problems. The technique used here is a generalization of the minimizing movements method, using two time-scales: one for velocity, the other (potentially much…

Numerical Analysis · Mathematics 2024-04-05 Antonín Češík , Sebastian Schwarzacher

Nested multi-step stochastic correction offers a possibility to improve updating algorithms for numerical simulations of lattice gauge theories with fermions. The corresponding generalisations of the two-step multi-boson (TSMB) algorithm as…

High Energy Physics - Lattice · Physics 2009-11-11 I. Montvay , E. Scholz

This paper considers spectral-difference methods of a high-order of accuracy for solving the one-way wave equation using the Laguerre integral transform with respect to time as the base. In order to provide a high spatial accuracy and…

Numerical Analysis · Mathematics 2018-05-10 Andrew V. Terekhov

Stochastic gradient methods are scalable for solving large-scale optimization problems that involve empirical expectations of loss functions. Existing results mainly apply to optimization problems where the objectives are one- or two-level…

Optimization and Control · Mathematics 2018-01-15 Shuoguang Yang , Mengdi Wang , Ethan X. Fang