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

Demand-based discrete optimization

We propose a methodological framework to include a wide variety of discrete choice models in (mixed) integer optimization problems. We succeed in obtaining a specification that is linear in the decision variables, allowing to use exact methods to solve the ...

2016

Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer

Christoph Koch, Immanuel Trummer

The D-Wave adiabatic quantum annealer solves hard combinatorial optimization problems leveraging quantum physics. The newest version features over 1000 qubits and was released in August 2015. We were given access to such a machine, currently hosted at NASA ...

Assoc Computing Machinery2016

Stochastic Three-Composite Convex Minimization

Volkan Cevher, Alp Yurtsever, Cong Bang Vu

We propose a stochastic optimization method for the minimization of the sum of three convex functions, one of which has Lipschitz continuous gradient as well as restricted strong convexity. Our approach is most suitable in the setting where it is computati ...

2016

Time-Optimal Path-Following Operation in the Presence of Uncertainty

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Predrag Milosavljevic

Path-following tasks, which refer to dynamic motion planning along pre-specified geometric references, are frequently encountered in applications such as milling, robot-supported measurements, and trajectory planning for autonomous vehicles. Different conv ...

Ieee2016

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

Nikolaos Geroliminis, Mohammadreza Saeedmanesh

Aggregated network level modeling and control of traffic in urban networks have recently gained a lot of interest due to unpredictability of travel behaviors and high complexity of physical modeling in microscopic level. Recent research has shown the exist ...

Elsevier2016

Mumford-Shah and Potts Regularization for Manifold-Valued Data

Martin Kurt Storath

Mumford-Shah and Potts functionals are powerful variational models for regularization which are widely used in signal and image processing; typical applications are edge-preserving denoising and segmentation. Being both non-smooth and non-convex, they are ...

Springer2016

MATHICSE Technical Report : A fast gradient method for nonnegative sparse regression with self dictionary

Robert Gerhard Jérôme Luce

Nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the input data matrix belong to the convex cone generated by only a few of its columns. The provably most robust met ...

MATHICSE2016

Scampi: a robust approximate message-passing framework for compressive imaging

Florent Gérard Krzakala, Jean François Emmanuel Barbier

Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabil ...

Iop Publishing Ltd2016

Subspace Acceleration For Large-Scale Parameter-Dependent Hermitian Eigenproblems

Daniel Kressner, Petar Sirkovic

This work is concerned with approximating the smallest eigenvalue of a parameter-dependent Hermitian matrix A(mu) for many parameter values mu in a domain D subset of R-P. The design of reliable and efficient algorithms for addressing this task is of impor ...

Siam Publications2016

Fast K-Means with Accurate Bounds

François Fleuret, James Peter Newling

We propose a novel accelerated exact

k

-means algorithm, which outperforms the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3

\times

faster. We also propose a general improvement of existing state-of-the-art a ...

Idiap2016

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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Demand-based discrete optimization

Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer

Stochastic Three-Composite Convex Minimization

Time-Optimal Path-Following Operation in the Presence of Uncertainty

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

Mumford-Shah and Potts Regularization for Manifold-Valued Data

MATHICSE Technical Report : A fast gradient method for nonnegative sparse regression with self dictionary

Scampi: a robust approximate message-passing framework for compressive imaging

Subspace Acceleration For Large-Scale Parameter-Dependent Hermitian Eigenproblems

Fast K-Means with Accurate Bounds

Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer

Mumford-Shah and Potts Regularization for Manifold-Valued Data

Demand-based discrete optimization

Clustering of heterogeneous networks with directional flows based on “Snake” similarities

MATHICSE Technical Report : A fast gradient method for nonnegative sparse regression with self dictionary

Scampi: a robust approximate message-passing framework for compressive imaging

Time-Optimal Path-Following Operation in the Presence of Uncertainty

Stochastic Three-Composite Convex Minimization

Fast K-Means with Accurate Bounds

Subspace Acceleration For Large-Scale Parameter-Dependent Hermitian Eigenproblems