Part of my work is concerned with applications in Sparse Bayesian Learning and therefore I occasionally stumble over interesting papers in the field of compressed sensing.
I recently read "Iteratively Reweighted Algorithms for Compressive Sensing" http://www.caam.rice.edu/~wy1/paperfiles/Rice_CAAM_TR08-01.PDF
The paper describes how using lp-norms with p<1 can be used to recover signals with fewer measurements than with the LASSO (L1-regularization).
There is even a wikipedia entry on Iteratively Reweighted Least Squares (IRLS).
However, I can't wrap my head around the difference between IRLS and Sequential Quadratic Programming (SQP). Is there any difference? The papers I have found on IRLS never mention SQP..
Many thanks in advance!