Randomised Algorithms for Large Scale Linear Algebra and Data Analytics, Colloquium Talk by PER-GUNNAR J. MARTINSSON

Institute for Digital Communications, School of Engineering, University of Edinburgh presents colloquium talk:Distributed Private Data Collection at Scale by Graham CormodeTea and Coffee served in CSEC from 1.30pm

Institute for Digital Communications, School of Engineering, University of Edinburgh

The talk will describe how methods based on randomised projections can be used to effectively, accurately, and reliably solve important problems that arise in data analytics and in large scale matrix computations. We will focus in particular on accelerated algorithms for computing full or partial matrix factorisations such as the eigenvalue decomposition, the QR factorisation, etc. Randomised projections are used in this context to reduce the effective dimensionality of intermediate steps in the computation. The resulting algorithms execute faster on modern hardware than traditional algorithms, and are particularly well suited for processing very large data sets.
 The methods described are supported by a rigorous mathematical analysis that exploits recent work in random matrix theory. The talk will briefly review some representative theoretical results.

James Clerk Maxwell Building

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Randomised Algorithms for Large Scale Linear Algebra and Data Analytics, Colloquium Talk by PER-GUNNAR J. MARTINSSON

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Randomised Algorithms for Large Scale Linear Algebra and Data Analytics, Colloquium Talk by PER-GUNNAR J. MARTINSSON

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