Abstract: Many real-world phenomena can be modeled as being sparse along some dimension. As such, by minimizing sparsity-promoting functionals such as the L1 norm or total variation, we can reconstruct signals from far-from-perfect data. This talk will provide an introductory overview of numerical algorithms for minimizing sparsity-promoting functionals, as well as applications to problems in audio processing and imaging science.