# Difference Between Iteratively Reweighted Least Squares (IRLS) and Sequential Quadratic Programming?

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 by Rick Chartrand and Wotao Yin (Available also on DocDroid).

The paper describes how using $${L}_{p}$$ -norms with $$p < 1$$ can be used to recover signals with fewer measurements than with the LASSO ($${L}_{1}$$ 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..