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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

On solving LPN using BKW and variants Implementation and Analysis

Serge Vaudenay, Sonia Mihaela Bogos, Florian Tramèr

The Learning Parity with Noise problem (LPN) is appealing in cryptography as it is considered to remain hard in the post-quantum world. It is also a good candidate for lightweight devices due to its simplicity. In this paper we provide a comprehensive anal ...

Springer2016

A New Framework for Distributed Submodular Maximization

Justin Dean Ward

A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algo ...

Ieee2016

Contribution to the Ultra-Fast Charging of Electric Vehicles

Rosa Martel Danoary Tsirinomeny

It is generally agreed that one of the drawbacks of a conventional vehicle is the inability to always reach the best operating point of the engine and the inability to regenerate the kinetic energy. Meaning, even with the help of the gearbox or automatic t ...

EPFL2016

Memetic Viability Evolution for Constrained Optimization

Dario Floreano, Andrea Maesani, Giovanni Iacca

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adapta- tion Evolution Strategy is one of the most efficient algorithms for unconstrai ...

Institute of Electrical and Electronics Engineers2016

A Bi-Criteria Approximation Algorithm for k-Means

Justin Dean Ward

We consider the classical k-means clustering problem in the setting of bi-criteria approximation, in which an algorithm is allowed to output beta*k > k clusters, and must produce a clustering with cost at most alpha times the to the cost of the optimal set ...

Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik2016

Never Say Never Probabilistic & Temporal Failure Detectors (Extended)

Rachid Guerraoui, David Kozhaya, Yvonne Anne Pignolet-Oswald

The failure detector approach for solving distributed computing problems has been celebrated for its modularity. This approach allows the construction of algorithms using abstract failure detection mechanisms, defined by axiomatic properties, as building b ...

IPDPS2016

Never Say Never Probabilistic & Temporal Failure Detectors

Rachid Guerraoui, David Kozhaya

Ieee2016

Observations on the LPN Solving Algorithm from Eurocrypt'16

Serge Vaudenay, Sonia Mihaela Bogos

In this note we re-evaluate the Eurocrypt’16 paper by Zhang et al. [9] in the area of LPN solving algorithms. We present the history of LPN solving algorithms and give the general description of the algorithm from [9]. While this new algorithm claims to im ...

2016

Modelling Resource Dependencies

Simon Bliudze, Alena Simalatsar, Alina Zolotukhina

The major research in the resource management literature focuses primarily on two directions: 1) specification languages for formulating resource requests and 2) constraint problems modelling allocation and scheduling. Both directions assume the knowledge ...

2016