Over the last year, there has been significant interest in solving many small linear algebra problems simultaneously. Library vendors such as MKL and NVIDIA, along with researchers at instutions including Manchester, Tennessee, and Sandia National Labs have all been attempting to perform these calculations as efficiently as possible.
Over the weekend prior to the SIAM CSE17 meeting, many of those researchers (including myself) held a workshop to discuss strategies for batched BLAS (basic linear algebra subprogram) computations. Furthermore, lots of discussion was aimed at standardising the function APIs and the memory layout that users will interact with. The slides, and a number of research papers on the topic, are available at this page.
At the SIAM CSE17 meeting, our team at Manchester organised a minisymposium to discuss the highlights of our weekend with a wider audience. A brief summary of the four talks, along with a copy of their slides, is given below.
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Last week Nick Higham, Edvin Deadman, and I ran a minisymposium on matrix functions at the SIAM Applied Linear Algebra 2015 conference (link). This post gives a brief summary of each talk, links to published work, and (once they appear) links to the slides with synchronised audio.
Edit: Links to the talks are now available.
Attendance at the sessions was very good, with some high-quality questions coming from the audience.
The symposium had two sessions.
- Marcel Schweitzer – Error Estimation in Krylov Subspace Methods for Matrix Functions
- Michele Benzi – Functions of Matrices with Kronecker Sum Structure
- Bruno Iannazzo – First-Order Riemannian Optimization Techniques for the Karcher Mean
- Sivan Toledo – A High Performance Algorithm for the Matrix Sign Function
- Peter Kandolf – The Leja Method: Backward Error Analysis and Implementation
- Massimiliano Fasi – An Algorithm for the Lambert W Function on Matrices
- Antii Koskela – An Exponential Integrator for Polynomially Perturbed Linear ODEs
- Edvin Deadman – Estimating the condition number of f(A)b
Peter Kandolf describing the famour “hump” in the matrix exponential.
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Recently I’ve been working with some of the statistics staff at the University of Manchester on sports analytics. Specifically we’ve been looking for useful models in football data. People from this background normally use R to analyze data and fit models.
Normally I would use Python for this kind of task but, since there was already a considerable amount of code in R, it made sense for me to do some work in R. The people at Continuum Analytics (who make the brilliant Anaconda Python distribution) recently announced support for R using their package manager conda. However, it wasn’t easy to find instructions to get a fully working environment, so here is what I did.
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I’ve finally finished! After years of reading papers, designing algorithms, hacking at code, and writing papers, my PhD is complete.
One of the most daunting thoughts I had as a PhD student was the idea of the viva: two experts sit in a room and pick apart the fine details of your work. They ask deep and technical questions, not limited merely to your thesis content, for a few hours (I’ve heard horror stories of 8 hours!) before sending you out of the room to discuss your fate. Fifteen minutes of palpitations later you get your result and (whatever the outcome) head to the pub, either to celebrate or drown your sorrows as appropriate.
In reality, because I was well prepared, my viva was actually just a chat with some knowledgeable people who were very interested in my work. There were a few curveball questions, nothing too serious, and the whole thing was done in an hour.
Here are some of my top tips for viva preparation.
The finished product!
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Every two years the IMA organize a conference on the interface between numerical linear algebra and optimization. For me, this was the perfect place to organize my first minisymposium entitled ‘Modern Directions for Matrix Analysis and Applications‘ with Natasa Strabic. We managed to get some great speakers talking about their ideas for future research. I’ve summarised some of their main ideas here and you can find my presentation on SlideShare.
Left to Right: Me, Amal Khabou, Ben Jeuris, Federico Poloni, Natasa Strabic, Roel Van Beeumen. Photo: Mario Berljafa.
Continue reading “Modern Directions for Matrix Analysis and Applications”