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- Title
A prediction-correction primal-dual hybrid gradient method for convex programming with linear constraints.
- Authors
Pibin Bing; Jialei Sui; Min Sun
- Abstract
In recent years, the primal-dual hybrid gradient (PDHG) method has been widely used. However, the original PDHG method may diverge without additional conditions. Here we propose a convergent predictioncorrection PDHG (PD-PDHG) method for canonical convex programming with linear constraints. The most important characteristic of the PD-PDHG method is that it adopts a new descent direction in the correction step, which does not converge to zero in general. Convergence of the new method is proved under mild assumptions. Finally, its efficiency is verified by compressive sensing.
- Subjects
COMPRESSED sensing; ITERATIVE methods (Mathematics); STOCHASTIC convergence; LAGRANGIAN functions; NUMBER theory
- Publication
ScienceAsia, 2018, Vol 44, Issue 1, p34
- ISSN
1513-1874
- Publication type
Article
- DOI
10.2306/scienceasia1513-1874.2018.44.034