Publications associées à Master theorem

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Daniel Kressner, Stefano Massei, Alice Cortinovis

This work is concerned with approximating matrix functions for banded matrices, hierarchically semiseparable matrices, and related structures. We develop a new divide-and-conquer method based on (rational) Krylov subspace methods for performing low-rank up ...

SIAM PUBLICATIONS2022

Paths in pseudorandom graphs and applications

Van Thang Pham

Let G = (V, E) be an (n, d, lambda)-graph. In this paper, we give an asymptotically tight condition on the size of U subset of V such that the number of paths of length k in U is close to the expected number for arbitrary integer k >= 1. More precisely, we ...

CHARLES BABBAGE RES CTR2020

Low-Rank Updates And A Divide-And-Conquer Method For Linear Matrix Equations

Daniel Kressner, Stefano Massei

Linear matrix equations, such as the Sylvester and Lyapunov equations, play an important role in various applications, including the stability analysis and dimensionality reduction of linear dynamical control systems and the solution of partial differentia ...

SIAM PUBLICATIONS2019

An adaptive primal-dual framework for nonsmooth convex minimization

Volkan Cevher, Quoc Tran Dinh, Ahmet Alacaoglu

We propose a new self-adaptive and double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov’s smoothing technique via general Bregman distance functions. It self-adaptiv ...

2019

Efficient preconditioning of hp-FEM matrices arising from time-varying problems: an application to topology optimization

Jan Sickmann Hesthaven, Paolo Gatto

We previously introduced a preconditioner that has proven effective for hp-FEM dis- cretizations of various challenging elliptic and hyperbolic problems. The construc- tion is inspired by standard nested dissection, and relies on the assumption that the Sc ...

Elsevier2017

MATHICSE Technical Report : Low-rank updates and a divideand- conquer method for linear matrix equations

Daniel Kressner, Stefano Massei

MATHICSE2017

Constrained LQR Using Online Decomposition Techniques

Colin Neil Jones, Georgios Stathopoulos

This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem without ...

2016

A Fast and Exact Algorithm for the Exemplar Breakpoint Distance

Bernard Moret, Mingfu Shao

A fundamental problem in comparative genomics is to compute the distance between two genomes. For two genomes without duplicate genes, we can easily compute a variety of distance measures in linear time, but the problem is NP-hard under most models when ge ...

Mary Ann Liebert, Inc2016

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

Martin Kurt Storath

The Mumford-Shah model is a very powerful variational approach for edge preserving regularization of image reconstruction processes. However, it is algorithmically challenging because one has to deal with a non-smooth and non-convex functional. In this pap ...

Iop Publishing Ltd2015

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Master theorem

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Paths in pseudorandom graphs and applications

Low-Rank Updates And A Divide-And-Conquer Method For Linear Matrix Equations

An adaptive primal-dual framework for nonsmooth convex minimization

Efficient preconditioning of hp-FEM matrices arising from time-varying problems: an application to topology optimization

MATHICSE Technical Report : Low-rank updates and a divideand- conquer method for linear matrix equations

Constrained LQR Using Online Decomposition Techniques

A Fast and Exact Algorithm for the Exemplar Breakpoint Distance

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

Graph Chatbot

Chattez avec Graph Search

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Paths in pseudorandom graphs and applications

Low-Rank Updates And A Divide-And-Conquer Method For Linear Matrix Equations

An adaptive primal-dual framework for nonsmooth convex minimization

Efficient preconditioning of hp-FEM matrices arising from time-varying problems: an application to topology optimization

MATHICSE Technical Report : Low-rank updates and a divideand- conquer method for linear matrix equations

Constrained LQR Using Online Decomposition Techniques

A Fast and Exact Algorithm for the Exemplar Breakpoint Distance

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging