Stephen Becker just sent me the following:
A month ago you mentioned a new survey paper that I wrote with Volkan Cevher and Mark Schmidt. It was designed as an introduction to some modern optimization techniques that are suitable for big data problems, with an emphasis on signal processing applications.
There is now a version of this on arXiv that is freely accessible to everyone. The link is
Thanks Stephen ! Here is the paper:
Convex Optimization for Big Data by Volkan Cevher, Stephen Becker, Mark Schmidt
This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques like first-order methods and randomization for scalability, and survey the important role of parallel and distributed computation. The new Big Data algorithms are based on surprisingly simple principles and attain staggering accelerations even on classical problems.
Related Reference Links include:
- The Big Picture in Compressive Sensing,
- The Advanced Matrix Factorization Jungle Page
- Randomized Numerical Linear Algebra (RandNLA)
- CAI: Cable And Igor's Adventures in Matrix Factorization
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