Publications related to Accelerated ADMM based on Accelerated Douglas-Rachford Splitting

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Fabio Nobile, Sebastian Krumscheid, Matthieu Claude Martin

We consider the numerical approximation of an optimal control problem for an elliptic Partial Differential Equation (PDE) with random coefficients. Specifically, the control function is a deterministic, distributed forcing term that minimizes the expected ...

2021

On the nonlinear Dirichlet-Neumann method and preconditioner for Newton's method

Tommaso Vanzan

The Dirichlet-Neumann (DN) method has been extensively studied for linear partial differential equations, while little attention has been devoted to the nonlinear case. In this paper, we analyze the DN method both as a nonlinear iterative method and as a p ...

Springer-Verlag2021

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Volkan Cevher, Ali Kavis

In this work we investigate stochastic non-convex optimization problems wherethe objective is an expectation over smooth loss functions, and the goal is to find an approximate stationary point. The most popular approach to handling such problems is varianc ...

2021

Computational Inverse Design of Surface-based Inflatables

Mark Pauly, Florin Isvoranu, Tian Chen

We present a computational inverse design method for a new class of surface-based inflatable structure. Our deployable structures are fabricated by fusing together two layers of inextensible sheet material along carefully selected curves. The fusing curves ...

ASSOC COMPUTING MACHINERY2021

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Efficient algorithms for wave problems

Boris Bonev

Wave phenomena manifest in nature as electromagnetic waves, acoustic waves, and gravitational waves among others.Their descriptions as partial differential equations in electromagnetics, acoustics, and fluid dynamics are ubiquitous in science and engineeri ...

EPFL2021

Large Steps in Inverse Rendering of Geometry

Wenzel Alban Jakob, Baptiste Alexandre Marie Philippe Claude Nicolet

Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to mi ...

ASSOC COMPUTING MACHINERY2021

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Lenka Zdeborová, Francesca Mignacco

In this paper we investigate how gradient-based algorithms such as gradient descent (GD), (multi-pass) stochastic GD, its persistent variant, and the Langevin algorithm navigate non-convex loss-landscapes and which of them is able to reach the best general ...

IOP PUBLISHING LTD2021

Multi space reduced basis preconditioners for parametrized Stokes equations

Alfio Quarteroni, Simone Deparis, Andrea Manzoni, Niccolò Dal Santo

We introduce a two-level preconditioner for the efficient solution of large scale saddle point linear systems arising from the finite element (FE) discretization of parametrized Stokes equations. This preconditioner extends the Multi Space Reduced Basis (M ...

PERGAMON-ELSEVIER SCIENCE LTD2019

On flexible Green function methods for atomistic/continuum coupling

Max Ludwig Hodapp

Atomistic/continuum (A/C) coupling schemes have been developed during the past twenty years to overcome the vast computational cost of fully atomistic models, but have not yet reached full maturity to address many problems of practical interest. This work ...

EPFL2018

Accelerated ADMM based on Accelerated Douglas-Rachford Splitting

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Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

On the nonlinear Dirichlet-Neumann method and preconditioner for Newton's method

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Computational Inverse Design of Surface-based Inflatables

A first-order primal-dual method with adaptivity to local smoothness

Efficient algorithms for wave problems

Large Steps in Inverse Rendering of Geometry

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Multi space reduced basis preconditioners for parametrized Stokes equations

On flexible Green function methods for atomistic/continuum coupling

On the nonlinear Dirichlet-Neumann method and preconditioner for Newton's method

Efficient algorithms for wave problems

On flexible Green function methods for atomistic/continuum coupling

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Large Steps in Inverse Rendering of Geometry

Computational Inverse Design of Surface-based Inflatables

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Multi space reduced basis preconditioners for parametrized Stokes equations

A first-order primal-dual method with adaptivity to local smoothness