• Brittan Farmer
  • January 26, 1 PM
  • 3866 East Hall

When compressing a signal, one wishes to express it accurately, concisely, and efficiently. Sparse approximation is one mathematical tool for studying compression. Last semester, I introduced three different sparse approximation problems and how greedy algorithms can be used to solve these problems. In this talk, I will describe convex relaxation, which allows tools from linear programming to be applied to sparse approximation problems. If time permits, I will discuss other types of algorithms and/or compressed sensing. This talk should be accessible to everyone.

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