Regularized Optimization Algorithm: Difference between revisions
Jump to navigation
Jump to search
m (Text replacement - ". ----" to ". ----") |
m (Text replacement - ". " to ". ") |
||
| Line 19: | Line 19: | ||
=== 2009 === | === 2009 === | ||
* ([[Goldstein & Osher, 2009]]) ⇒ [[Tom Goldstein]], and [[Stanley Osher]]. ([[2009]]). “The Split Bregman Method for L1-regularized Problems.” In: SIAM Journal on Imaging Sciences, 2(2). | * ([[Goldstein & Osher, 2009]]) ⇒ [[Tom Goldstein]], and [[Stanley Osher]]. ([[2009]]). “The Split Bregman Method for L1-regularized Problems.” In: SIAM Journal on Imaging Sciences, 2(2). | ||
** QUOTE: ... The class of [[L1-regularized optimization problem]]s has received much attention recently because of the introduction of “compressed sensing,” which allows images and signals to be reconstructed from small amounts of data. ... | ** QUOTE: ... The class of [[L1-regularized optimization problem]]s has received much attention recently because of the introduction of “compressed sensing,” which allows images and signals to be reconstructed from small amounts of data. ... | ||
---- | ---- | ||
Latest revision as of 13:26, 2 August 2022
A Regularized Optimization Algorithm is an optimization algorithm that makes use of a regularization term.
- AKA: Regularized Regression Method.
- Context:
- It can be applied by a Regularized Optimization System (to solve a regularized optimization task).
- Example(s):
- Counter-Example(s):
- See: Regularized Least-Squares Function Fitting Algorithm.
References
2009
- (Goldstein & Osher, 2009) ⇒ Tom Goldstein, and Stanley Osher. (2009). “The Split Bregman Method for L1-regularized Problems.” In: SIAM Journal on Imaging Sciences, 2(2).
- QUOTE: ... The class of L1-regularized optimization problems has received much attention recently because of the introduction of “compressed sensing,” which allows images and signals to be reconstructed from small amounts of data. ...