Bregman Proximal Methods for Semidefinite Optimization

Abstract: Generalized proximal methods based on Bregman distances offer the possibility of matching the distance to the structure in the problem, with the goal of reducing the complexity per iteration. In semidefinite optimization, the use of a generalized distance can allow us to avoid expensive eigendecompositions, needed in standard proximal methods for Euclidean projections on the positive semidefinite cone. We discuss applications to sparse semidefinite optimization, and to other types of structure that are common in control and signal processing, such as Toeplitz structure.

optimization and control

Audience: advanced learners

Comments: The address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk

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One World Optimization seminar

Series comments: Description: Online seminar on optimization and related areas

The address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk

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