3 papers
math.NA2009
Accelerating gradient projection methods for -constrained signal recovery by steplength selection rules
I. Loris, M. Bertero, C. De Mol +2
We propose a new gradient projection algorithm that compares favorably with the fastest algorithms available to date for -constrained sparse recovery from noisy data, both…
q-fin.PM2007
Sparse and stable Markowitz portfolios
Joshua Brodie, Ingrid Daubechies, Christine De Mol +2
We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to…
math.FA2003
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
Ingrid Daubechies, Michel Defrise, Christine De Mol
We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary pre-assigned orthonormal basis. We prove that replacing the usual quadr…