##### Department of Mathematics,

University of California San Diego

****************************

### Math 295 - Mathematics Colloquium

## Michael P. Friedlander

#### University of British Columbia

## Algorithms for large-scale sparse reconstruction

##### Abstract:

Many signal-processing applications seek to approximate a signal as a superposition of only a few elementary atoms drawn from a large collection. This is known as sparse reconstruction. The theory of compressed sensing allows us to pose sparse reconstruction problems as structured convex optimization problems. I will discuss the role of duality in revealing some unexpected and useful properties of these problems, and will show how they lead to practical, large-scale algorithms. I will also describe some applications of the resulting algorithms.

Host: Philip Gill

### February 19, 2009

### 3:00 PM

### AP&M 6402

****************************