Publications related to Matrix Recipes for Hard Thresholding Methods

Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

We propose an online algorithm for sequential learning in the contextual multiarmed bandit setting. Our approach is to partition the context space and, then, optimally combine all of the possible mappings between the partition regions and the set of bandit ...

2019

The Complexity of Splitting Necklaces and Bisecting Ham Sandwiches

Aris Filos Ratsikas

We resolve the computational complexity of two problems known as NECKLACE-SPLITTING and DISCRETE HAM SANDWICH, showing that they are PPA-complete. For NECKLACE SPLITTING, this result is specific to the important special case in which two thieves share the ...

ACM2019

Coordinate-Descent Diffusion Learning by Networked Agents

Ali H. Sayed, Bicheng Ying, Chengcheng Wang

This paper examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gr ...

Ieee-Inst Electrical Electronics Engineers Inc2018

The complexity of error metrics

Mathias Soeken

Approximate computing exploits the fact that many applications do not require the results to be exact but not to exceed a threshold in a given error metric. Algorithms in approximate computing require to compute the error of the approximation in order to m ...

ELSEVIER SCIENCE BV2018

Coordinate-descent adaptation over networks

Ali H. Sayed, Bicheng Ying, Chengcheng Wang

This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gra ...

2017

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

A Comment on “Computational Complexity of Stochastic Programming Problems”

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Mathematical Programming A, 106(3):423–432, 200 ...

Springer Heidelberg2016

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

We propose to approximate two-stage distributionally robust programs with binary recourse decisions by their associated K-adaptability problems, which pre-select K candidate second-stage policies here-and-now and implement the best of these policies once t ...

Elsevier Science Bv2016

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

Tight Bounds for Asynchronous Renaming

Rachid Guerraoui, Seth Gilbert, Dan Alistarh

This article presents the first tight bounds on the time complexity of shared-memory renaming, a fundamental problem in distributed computing in which a set of processes need to pick distinct identifiers from a small namespace. We first prove an individual ...

Association for Computing Machinery2014

Matrix Recipes for Hard Thresholding Methods

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Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

The Complexity of Splitting Necklaces and Bisecting Ham Sandwiches

Coordinate-Descent Diffusion Learning by Networked Agents

The complexity of error metrics

Coordinate-descent adaptation over networks

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

A Comment on “Computational Complexity of Stochastic Programming Problems”

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

On solving LPN using BKW and variants Implementation and Analysis

Tight Bounds for Asynchronous Renaming

Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

The Complexity of Splitting Necklaces and Bisecting Ham Sandwiches

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Coordinate-Descent Diffusion Learning by Networked Agents

The complexity of error metrics

On solving LPN using BKW and variants Implementation and Analysis

Coordinate-descent adaptation over networks

A Comment on “Computational Complexity of Stochastic Programming Problems”

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

Tight Bounds for Asynchronous Renaming