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In this paper we present a new steepest-descent type algorithm for convex optimization problems. Our algorithm pieces the unknown into sub-blocs of unknowns and considers a partial optimization over each sub-bloc. In quadratic optimization,…

Optimization and Control · Mathematics 2015-01-15 Mohamed Kamel Riahi

Joint diagonalization, the process of finding a shared set of approximate eigenvectors for a collection of matrices, arises in diverse applications such as multidimensional harmonic analysis or quantum information theory. This task is…

Optimization and Control · Mathematics 2025-02-12 Erik Troedsson , Marcus Carlsson , Herwig Wendt

This is the first in a series of papers in which we study an efficient approximation scheme for solving the Hamilton-Jacobi-Bellman equation for multi-dimensional problems in stochastic control theory. The method is a combination of a WKB…

Computational Finance · Quantitative Finance 2014-06-26 Sakda Chaiworawitkul , Patrick S. Hagan , Andrew Lesniewski

A fully parallel version of the contact dynamics (CD) method is presented in this paper. For large enough systems, 100% efficiency has been demonstrated for up to 256 processors using a hierarchical domain decomposition with dynamic load…

Soft Condensed Matter · Physics 2015-03-19 Zahra Shojaaee , M. Reza Shaebani , Lothar Brendel , János Török , Dietrich E. Wolf

This paper introduces a novel approach for the construction of bulk--surface splitting schemes for semi-linear parabolic partial differential equations with dynamic boundary conditions. The proposed construction is based on a reformulation…

Numerical Analysis · Mathematics 2023-07-06 R. Altmann , C. Zimmer

We shall derive and propose several efficient overlapping domain decomposition methods for solving some typical linear inverse problems, including the identiffication of the flux, the source strength and the initial temperature in second…

Numerical Analysis · Mathematics 2013-09-10 Jiang Daijun , Feng Hui , Zou Jun

As the number of processor cores on supercomputers becomes larger and larger, algorithms with high degree of parallelism attract more attention. In this work, we propose a novel space-time coupled algorithm for solving an inverse problem…

Numerical Analysis · Computer Science 2015-08-26 Xiaomao Deng , Xiao-chuan Cai , Jun Zou

We introduce a new and efficient numerical method for multicriterion optimal control and single criterion optimal control under integral constraints. The approach is based on extending the state space to include information on a "budget"…

Optimization and Control · Mathematics 2016-01-06 Ajeet Kumar , Alexander Vladimirsky

In this contribution, a finite element scheme to impose mixed boundary conditions without introducing Lagrange multipliers is presented for hyperbolic systems described as port-Hamiltonian systems. The strategy relies on finite element…

Numerical Analysis · Mathematics 2026-01-23 S. D. M. de Jong , A. Brugnoli , R. Rashad , Y. Zhang , S. Stramigioli

In this paper, we propose two novel Robin-type domain decomposition methods based on the two-grid techniques for the coupled Stokes-Darcy system. Our schemes firstly adopt the existing Robin-type domain decomposition algorithm to obtain the…

Numerical Analysis · Mathematics 2021-08-11 Yizhong Sun , Feng Shi , Haibiao Zheng , Heng Li , Fan Wang

We propose a new method for the problem of controlling linear dynamical systems under partial observation and adversarial disturbances. Our new algorithm, Double Spectral Control (DSC), matches the best known regret guarantees while…

Machine Learning · Computer Science 2025-05-28 Anand Brahmbhatt , Gon Buzaglo , Sofiia Druchyna , Elad Hazan

A novel discontinuous Galerkin (DG) method is developed to solve time-dependent bi-harmonic type equations involving fourth derivatives in one and multiple space dimensions. We present the spatial DG discretization based on a mixed…

Numerical Analysis · Mathematics 2019-10-02 Hailiang Liu , Peimeng Yin

As a main step in the numerical solution of control problems in continuous time, the controlled process is approximated by sequences of controlled Markov chains, thus discretising time and space. A new feature in this context is to allow…

Optimization and Control · Mathematics 2007-05-23 Markus Fischer , Markus Reiss

We present a novel multiscale numerical approach that combines parallel-in-time computation with hybrid domain adaptation for linear collisional kinetic equations in the diffusive regime. The method addresses the computational challenges of…

Numerical Analysis · Mathematics 2025-11-18 Tino Laidin

The numerical solution of large-scale PDEs, such as those occurring in data-driven applications, unavoidably require powerful parallel computers and tailored parallel algorithms to make the best possible use of them. In fact, considerations…

Numerical Analysis · Mathematics 2017-05-11 Francisco Bernal , Gonçalo dos Reis , Greig Smith

This paper is concerned with developing and analyzing two novel implicit temporal discretization methods for the stochastic semilinear wave equations with multiplicative noise. The proposed methods are natural extensions of well-known…

Numerical Analysis · Mathematics 2024-08-26 Xiaobing Feng , Yukun Li , Liet Vo

This work proposes a novel numerical scheme for solving the high-dimensional Hamilton-Jacobi-Bellman equation with a functional hierarchical tensor ansatz. We consider the setting of stochastic control, whereby one applies control to a…

Numerical Analysis · Mathematics 2025-07-01 Xun Tang , Nan Sheng , Lexing Ying

The purpose of this work is to study an optimal control problem for a semilinear elliptic partial differential equation with a linear combination of Dirac measures as a forcing term; the control variable corresponds to the amplitude of such…

Optimization and Control · Mathematics 2023-07-04 Enrique Otarola

The fully discrete adjoint equations and the corresponding adjoint method are derived for a globally high- order accurate discretization of conservation laws on parametrized, deforming domains. The conservation law on the deforming domain…

Optimization and Control · Mathematics 2016-09-20 Matthew J. Zahr , Per-Olof Persson

This work studies the linear approximation of high-dimensional dynamical systems using low-rank dynamic mode decomposition (DMD). Searching this approximation in a data-driven approach is formalised as attempting to solve a low-rank…

Machine Learning · Statistics 2021-08-23 Patrick Héas , Cédric Herzet