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A general double-proximal gradient algorithm for d.c. programming Bot, Radu Ioan
Description
The possibilities of exploiting the special structure of d.c. programs, which
consist of optimizing the difference of convex functions, are currently more
or less limited to variants of the DCA proposed by Pham Dinh Tao and Le
Thi Hoai An in 1997. These assume that either the convex or the concave
part, or both, are evaluated by one of their subgradients.
In this talk we propose an algorithm which allows the evaluation of both
the concave and the convex part by their proximal points. Additionally, we
allow a smooth part, which is evaluated via its gradient. In the spirit of
primal-dual splitting algorithms, the concave part might be the composition
of a concave function with a linear operator, which are, however, evaluated
separately.
For this algorithm we show that every cluster point is a solution of the
optimization problem. Furthermore, we show the connection to the Toland
dual problem and prove a descent property for the objective function values
of a primal-dual formulation of the problem. Convergence of the iterates is
shown if this objective function satisfies the Kurdyka - Lojasiewicz property.
In the last part, we apply the algorithm to an image processing model.
The talk relies on a joint work with Sebastian Banert.
Item Metadata
Title |
A general double-proximal gradient algorithm for d.c. programming
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2017-09-19T17:37
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Description |
The possibilities of exploiting the special structure of d.c. programs, which
consist of optimizing the difference of convex functions, are currently more
or less limited to variants of the DCA proposed by Pham Dinh Tao and Le
Thi Hoai An in 1997. These assume that either the convex or the concave
part, or both, are evaluated by one of their subgradients.
In this talk we propose an algorithm which allows the evaluation of both the concave and the convex part by their proximal points. Additionally, we allow a smooth part, which is evaluated via its gradient. In the spirit of primal-dual splitting algorithms, the concave part might be the composition of a concave function with a linear operator, which are, however, evaluated separately. For this algorithm we show that every cluster point is a solution of the optimization problem. Furthermore, we show the connection to the Toland dual problem and prove a descent property for the objective function values of a primal-dual formulation of the problem. Convergence of the iterates is shown if this objective function satisfies the Kurdyka - Lojasiewicz property. In the last part, we apply the algorithm to an image processing model. The talk relies on a joint work with Sebastian Banert. |
Extent |
39 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: University of Vienna
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Series | |
Date Available |
2018-03-25
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0364458
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International