Publications related to Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Faster Algorithms for Integer Programs with Block Structure

Friedrich Eisenbrand, Kim-Manuel Klein, Christoph Hunkenschröder

We consider integer programming problems max {c^Tx : A x = b, l

Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik2018

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

We propose a conditional gradient framework for a composite convex minimization template with broad applications. Our approach combines the notions of smoothing and homotopy under the CGM framework, and provably achieves the optimal O(1/sqrt(k)) convergenc ...

ICML2018

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Hoài-Minh Nguyên

We present new results concerning the approximation of the total variation,

\int_{\Omega} |\nabla u|

, of a function

u

by non-local, non-convex functionals of the form $$ \Lambda_\delta u = \int_{\Omega} \int_{\Omega} \frac{\delta \varphi \big( |u(x) - ...

2018

Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls

Daniel Kuhn, Grani Adiwena Hanasusanto

Adaptive robust optimization problems are usually solved approximately by restricting the adaptive decisions to simple parametric decision rules. However, the corresponding approximation error can be substantial. In this paper we show that two-stage robust ...

2018

Asynchronous updates for stochastic gradient descent

Alberto Silvio Chiappa

Finding convergence rates for numerical optimization algorithms is an important task, because it gives a justification to their use in solving practical problems, while also providing a way to compare their efficiency. This is especially useful in an async ...

2017

Dynamic Facility Location via Exponential Clocks

Ola Nils Anders Svensson, Ashkan Norouzi Fard, Hyung Chan An

The dynamic facility location problem is a generalization of the classic facility location problem proposed by Eisenstat, Mathieu, and Schabanel to model the dynamics of evolving social/infrastructure networks. The generalization lies in that the distance ...

Association for Computing Machinery2017

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

Michaël Unser, Demetri Psaltis, Ulugbek Kamilov, Alexandre Goy, Joowon Lim

Learning Tomography (LT) is a nonlinear optimization algorithm for computationally imaging three-dimensional (3D) distribution of the refractive index in semi-transparent samples. Since the energy function in LT is generally non-convex, the solution it obt ...

Ieee2017

Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks

Nikolaos Geroliminis, Mohammadreza Saeedmanesh

The problem of clustering in urban traffic networks has been mainly studied in static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be ...

2017

Technical Report: Voltage Source Converter MPC with Optimized Pulse Patterns and minimization of Integrated Squared Tracking Error

Ivan Pejcic, Helfried Peyrl

Model predictive control schemes for power electronic applications are characterized by a great variety of problem formulations. In this paper, we consider a three phase voltage source converter with an arbitrary number of voltage levels and derive a model ...

2017

A Data-Driven Approach to Power Converter Control via Convex Optimization

Alireza Karimi, Achille Nicoletti, Michele Martino

A new model reference data-driven approach is presented for synthesizing controllers for the CERN power converter control system. This method uses the frequency re- sponse function (FRF) of a system in order to avoid the problem of unmodeled dynamics assoc ...

2017

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Graph Chatbot

Chat with Graph Search

Faster Algorithms for Integer Programs with Block Structure

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls

Asynchronous updates for stochastic gradient descent

Dynamic Facility Location via Exponential Clocks

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks

Technical Report: Voltage Source Converter MPC with Optimized Pulse Patterns and minimization of Integrated Squared Tracking Error

A Data-Driven Approach to Power Converter Control via Convex Optimization

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Dynamic Facility Location via Exponential Clocks

Faster Algorithms for Integer Programs with Block Structure

A Data-Driven Approach to Power Converter Control via Convex Optimization

Asynchronous updates for stochastic gradient descent

Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Technical Report: Voltage Source Converter MPC with Optimized Pulse Patterns and minimization of Integrated Squared Tracking Error