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We propose an {\em implementable} numerical scheme for the discretization of linear-quadratic optimal control problems involving SDEs in higher dimensions with {\em control constraint}. For time discretization, we employ the implicit Euler…

Analysis of PDEs · Mathematics 2024-12-12 Abhishek Chaudhary

Most simulation schemes for partial differential equations (PDEs) focus on minimizing a simple error norm of a discretized version of a field. This paper takes a fundamentally different approach; the discretized field is interpreted as data…

Methodology · Statistics 2018-04-18 Reimar H. Leike , Torsten A. Enßlin

Despite the practicality of quantile regression (QR), simultaneous estimation of multiple QR curves continues to be challenging. We address this problem by proposing a Bayesian nonparametric framework that generalizes the quantile pyramid…

Methodology · Statistics 2023-11-30 Hyoin An , Steven N. MacEachern

Quadratization for partial differential equations (PDEs) is a process that transforms a nonquadratic PDE into a quadratic form by introducing auxiliary variables. This symbolic transformation has been used in diverse fields to simplify the…

Symbolic Computation · Computer Science 2026-02-27 Albani Olivieri , Gleb Pogudin , Boris Kramer

In this paper we propose a numerical scheme for the class of backward doubly stochastic (BDSDEs) with possible path-dependent terminal values. We prove that our scheme converge in the strong $L^2$-sense and derive its rate of convergence.…

Probability · Mathematics 2011-08-04 Auguste Aman

This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process (PP's) models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon…

Methodology · Statistics 2012-01-24 James S. Martin , Ajay Jasra , Emma McCoy

With the rising popularity of Large Language Models (LLMs), there has been an increasing interest in compression techniques that enable their efficient deployment. This study focuses on the Post-Training Quantization (PTQ) of LLMs. Drawing…

Machine Learning · Statistics 2023-12-04 Kayhan Behdin , Ayan Acharya , Aman Gupta , Qingquan Song , Siyu Zhu , Sathiya Keerthi , Rahul Mazumder

This paper proposes approaches for the analysis of multiple changepoint models when dependency in the data is modelled through a hierarchical Gaussian Markov random field. Integrated nested Laplace approximations are used to approximate…

Computation · Statistics 2015-03-17 Jason Wyse , Nial Friel , Håvard Rue

Fractional polynomials are widely used for dose-response modelling, and recent Bayesian fractional polynomial work has renewed interest in this finite model class. We propose PMM-FP, a frequentist extension of Kunchenko's polynomial…

Methodology · Statistics 2026-05-26 Serhii Zabolotnii

Quantized neural networks are well known for reducing the latency, power consumption, and model size without significant harm to the performance. This makes them highly appropriate for systems with limited resources and low power capacity.…

Machine Learning · Computer Science 2024-06-11 Moshe Kimhi , Tal Rozen , Avi Mendelson , Chaim Baskin

In this article we design a novel quasi-regression Monte Carlo algorithm in order to approximate the solution of discrete time backward stochastic differential equations (BSDEs), and we analyze the convergence of the proposed method. The…

Numerical Analysis · Mathematics 2024-08-01 E. Gobet , J. G. López-Salas , C. Vázquez

Mathematical models, calibrated to data, have become ubiquitous to make key decision processes in modern quantitative finance. In this work, we propose a novel framework for data-driven model selection by integrating a classical…

Computational Finance · Quantitative Finance 2020-06-04 Imanol Perez Arribas , Cristopher Salvi , Lukasz Szpruch

We study the numerical approximation of linear-quadratic optimal control problems subject to the fractional Laplace equation with its spectral definition. We compute an approximation of the state equation using a discretization of the…

Numerical Analysis · Mathematics 2018-09-28 Stefan Dohr , Christian Kahle , Sergejs Rogovs , Piotr Swierczynski

We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial…

Methodology · Statistics 2020-09-09 Laura Jula Vanegas , Merle Behr , Axel Munk

Quantum ensemble systems arise in a variety of applications, including NMR spectroscopy and robust quantum control. While their theoretical properties have been extensively studied, relatively little attention has been given to the explicit…

Optimization and Control · Mathematics 2025-06-25 Ruikang Liang , Gong Cheng

Iterative multiscale methods for electronic structure calculations offer several advantages for large-scale problems. Here we examine a nonlinear full approximation scheme (FAS) multigrid method for solving fixed potential and…

Materials Science · Physics 2007-05-23 Nimal Wijesekera , Guogang Feng , Thomas L. Beck

In this article, we concern a kind of partially observed non-zero sum stochastic differential game based on forward and backward stochastic differential equations (FBSDEs). It is required that each player has his own observation equation,…

Optimization and Control · Mathematics 2016-01-05 Jie Xiong , Shuaiqi Zhang , Yi Zhuang

We study the discrete-time approximation for solutions of forward-backward stochas- tic dierential equations (FBSDEs) with a jump. In this part, we study the case of Lipschitz generators, and we refer to the second part of this work [15]…

Analysis of PDEs · Mathematics 2012-11-28 Idris Kharroubi , Thomas Lim

Fast and accurate simulation of dynamical systems is a fundamental challenge across scientific and engineering domains. Traditional numerical integrators often face a trade-off between accuracy and computational efficiency, while existing…

Computational Engineering, Finance, and Science · Computer Science 2026-03-06 Jiaxin Yuan , Haizhao Yang , Maria Cameron

Functional approaches are the only first principle QCD setup that allow for direct computations at finite density. Predictive power and quantitative reliability of the respective results can only be obtained within a systematic expansion…

High Energy Physics - Phenomenology · Physics 2024-08-19 Friederike Ihssen , Jan M. Pawlowski , Franz R. Sattler , Nicolas Wink
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