Minimization of transformed L1 penalty: theory, difference of convex function algorithm, and robust application in compressed sensing

Title
Minimization of transformed L1 penalty: theory, difference of convex function algorithm, and robust application in compressed sensing
Authors
Keywords
Transformed <span class=InlineEquation id=IEq18>(l_1), Sparse signal recovery theory, Difference of convex function algorithm, Convergence analysis, Coherent random matrices, Compressed sensing, Robust recovery, 90C26, 65K10, 90C90
Journal
MATHEMATICAL PROGRAMMING
Volume 169, Issue 1, Pages 307-336
Publisher
Springer Nature
Online
2018-03-05
DOI
10.1007/s10107-018-1236-x

Ask authors/readers for more resources

Reprint

Contact the author

Find Funding. Review Successful Grants.

Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.

Explore

Find the ideal target journal for your manuscript

Explore over 38,000 international journals covering a vast array of academic fields.

Search