Concept

Newton's method in optimization

Related publications (163)

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

Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking

Pascal Fua, Andrii Maksai

Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease t ...

IEEE2019

A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

Decentralized optimization is a powerful paradigm that finds applications in engineering and learning design. This work studies decentralized composite optimization problems with non-smooth regularization terms. Most existing gradient-based proximal decent ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Efficient Greedy Coordinate Descent for Composite Problems

Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova, Sai Praneeth Reddy Karimireddy

Coordinate descent with random coordinate selection is the current state of the art for many large scale optimization problems. However, greedy selection of the steepest coordinate on smooth problems can yield convergence rates independent of the dimension ...

2019

Efficient Greedy Coordinate Descent for Composite Problems

Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova, Sai Praneeth Reddy Karimireddy

MICROTOME PUBLISHING2019

System and method for performing isogeometric analysis and finite element analysis using h-splines

Michaël Unser, Zsuzsanna Püspöki, Daniel Andreas Schmitter

A method for performing H-spline compatible isogeometric analysis (IGA) on a geometric object, the method being performed on a computer device having a memory, the method including the steps of creating an analysis suitable geometry (ASG) using H-splines f ...

2019

Constrained optimization applied to multiscale integrative modeling

Giorgio Elikem Tamo

Multiscale integrative modeling stands at the intersection between experimental and computational techniques to predict the atomistic structures of important macromolecules. In the integrative modeling process, the experimental information is often integra ...

EPFL2018

Performance limits of stochastic sub-gradient learning, Part II: Multi-agent case

Ali H. Sayed, Bicheng Ying

The analysis in Part I [1] revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization. These algorithms are used when the risk functions are non-smooth or involve non-differentiable components. They have b ...

Elsevier2018

SNM: Stochastic Newton Method for Optimization of Discrete Choice Models

Michel Bierlaire, Virginie Janine Camille Lurkin, Gael Lederrey

Optimization algorithms are rarely mentioned in the discrete choice literature. One reason may be that classic Newton-Raphson methods have been rather successful in estimating discrete choice parameters on available data sets of limited size. However, than ...

2018

SNM: Stochastic Newton Methodfor Optimization of Discrete Choice Models

Michel Bierlaire, Virginie Janine Camille Lurkin, Gael Lederrey

IEEE2018

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