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“…If strong convexity is assumed, mirror descent [8,45] can also be interpreted as gradient descent on the dual problem, and its regularization properties (and those of its accelerated variant) have been studied in [42]. Diagonal approaches [3] with a regularization parameter that vanishes along the iterations have been studied in [36], see [20] for an accelerated version. Another common approach relies on the linearized Bregman iteration [47,66,68,69], which has found applications in compressed sensing [19,48,69] and image deblurring [19].…”
Section: Related Workmentioning
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“…If strong convexity is assumed, mirror descent [8,45] can also be interpreted as gradient descent on the dual problem, and its regularization properties (and those of its accelerated variant) have been studied in [42]. Diagonal approaches [3] with a regularization parameter that vanishes along the iterations have been studied in [36], see [20] for an accelerated version. Another common approach relies on the linearized Bregman iteration [47,66,68,69], which has found applications in compressed sensing [19,48,69] and image deblurring [19].…”
Section: Related Workmentioning
“…The assumptions on the noise are the classical ones in inverse problems, see e.g. [17,20,42,43]. We generalize the results in [43], by including in the primal-dual procedure a diagonal preconditioning and an extra activation step.…”
Section: Introductionmentioning
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