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This work introduces a preconditioned dual optimization framework with the alternating direction method of multipliers (ADMM) to the optical flow estimates. By introducing efficient preconditioners with the multiscale pyramid, our preconditioned algorithms give competitive optical flow estimates under appropriate variational functional frameworks. We propose a novel preconditioned alternating direction methods of multipliers (ADMM) with convergenceguarantee for the total variation regularized optical flow problem through optimizing the dual problems. The numerical tests show the proposed preconditioned ADMM algorithms are very efficient for the total variation regularized optical flow estimates.
Quantization of the parameters of machine learning models, such as deep neural networks, requires solving constrained optimization problems, where the constraint set is formed by the Cartesian product of many simple discrete sets. For such optimizati
Ptychography has risen as a reference X-ray imaging technique: it achieves resolutions of one billionth of a meter, macroscopic field of view, or the capability to retrieve chemical or magnetic contrast, among other features. A ptychographyic reconst
We consider a network of agents that are cooperatively solving a global optimization problem, where the objective function is the sum of privately known local objective functions of the agents and the decision variables are coupled via linear constra
In this paper, we develop a dual alternating direction method of multipliers (ADMM) for an image decomposition model. In this model, an image is divided into two meaningful components, i.e., a cartoon part and a texture part. The optimization algorit
The alternating direction method of multipliers (ADMM) is one of the most widely used first-order optimisation methods in the literature owing to its simplicity, flexibility and efficiency. Over the years, numerous efforts are made to improve the per