Publications related to Convex hull algorithms

Generalized convex hull construction for materials discovery

Michele Ceriotti, Edgar Albert Engel, Andrea Anelli

Searching for novel materials involves identifying potential candidates and selecting those that have desirable properties and facile synthesis. It is relatively easy to generate large numbers of potential candidates, for instance, by computational searche ...

2018

Representing groups against all odds

Maxime Gheysens

We investigate how probability tools can be useful to study representations of non-amenable groups. A suitable notion of "probabilistic subgroup" is proposed for locally compact groups, and is valuable to induction of representations. Nonamenable groups ad ...

EPFL2017

Robust control of constrained systems given an information structure

Maryam Kamgarpour, Luca Furieri

We study finite horizon optimal control where the controller is subject to sensor-information constraints, that is, each input has access to a fixed subset of states at all times. In particular, we consider linear systems affected by exogenous disturbances ...

IEEE2017

GIFT: A Small Present Towards Reaching the Limit of Lightweight Encryption

Subhadeep Banik

In this article, we revisit the design strategy of PRESENT, leveraging all the advances provided by the research community in construction and cryptanalysis since its publication, to push the design up to its limits. We obtain an improved version, named GI ...

2017

Efficient Algorithms for the Data Exchange Problem

Michael Christoph Gastpar, Sameer Pawar

In this paper, we study the data exchange problem, where a set of users is interested in gaining access to a common file, but where each has only partial knowledge about it as side-information. Assuming that the file is broken into packets, the side-inform ...

Institute of Electrical and Electronics Engineers2016

Convex block-sparse linear regression with expanders - provably

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis, Luca Baldassarre, Bubacarr Bah

Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-ti ...

2016

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

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Volkan Cevher, Quoc Tran Dinh, Alp Yurtsever, Yen-Huan Li, Ya-Ping Hsieh, Marwa El Halabi, Gergely Odor

We study a phase retrieval problem in the Poisson noise model. Motivated by the PhaseLift approach, we approximate the maximum-likelihood estimator by solving a convex program with a nuclear norm constraint. While the Frank-Wolfe algorithm, together with t ...

Ieee2016

Guaranteeing Input Tracking For Constrained Systems: Theory and Application to Demand Response

Colin Neil Jones, Tomasz Tadeusz Gorecki, Georgios Stathopoulos, Altug Bitlislioglu

A method for certifying exact input trackability for constrained discrete time linear systems is introduced in this paper. A signal is assumed to be drawn from a reference set and the system must track this signal with a linear combination of its inputs. U ...

2015

Selling robustness margins: A framework for optimizing reserve capacities for linear systems

Maryam Kamgarpour, Xiuwei Zhang

This paper proposes a method for solving robust optimal control problems with modulated uncertainty sets. We consider constrained uncertain linear systems and interpret the uncertainty sets as “robustness margins” or “reserve capacities”. In particular, gi ...

IEEE2014

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Generalized convex hull construction for materials discovery

Representing groups against all odds

Robust control of constrained systems given an information structure

GIFT: A Small Present Towards Reaching the Limit of Lightweight Encryption

Efficient Algorithms for the Data Exchange Problem

Convex block-sparse linear regression with expanders - provably

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

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Guaranteeing Input Tracking For Constrained Systems: Theory and Application to Demand Response

Selling robustness margins: A framework for optimizing reserve capacities for linear systems

Robust control of constrained systems given an information structure

Representing groups against all odds

GIFT: A Small Present Towards Reaching the Limit of Lightweight Encryption

Efficient Algorithms for the Data Exchange Problem

Generalized convex hull construction for materials discovery

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

Convex block-sparse linear regression with expanders - provably

Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval

Guaranteeing Input Tracking For Constrained Systems: Theory and Application to Demand Response

Selling robustness margins: A framework for optimizing reserve capacities for linear systems