0
$\begingroup$

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!

$\endgroup$
2
$\begingroup$

SQP is a method for solving smooth (objective and constraint functions are at least twice differentiable) constrained nonlinear optimization problems. It solves a series of quadratic programming problems to converge to a solution to the Karush-Kuhn-Tucker conditions for the constrained optimization problem.

IRLS is a method for solving unconstrained minimization problems by solving a sequence of least squares problems which are obtained from the original problem by a rescaling at each iteration.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.