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Primal-Dual Iterations for Efficient Solutions of Nonsmooth Variational Problems

This study presents a novel approach using primal-dual iterations to address nonsmooth variational problems effectively. By leveraging a convex saddle-point formulation, we achieve significant improvements in numerical robustness and speed. Our method is particularly suitable for relaxed combinatorial problems, providing solutions that are up to 10 times faster than traditional methods. The findings are supported by research conducted at the University of Heidelberg by J. Lellmann, D. Breitenreicher, and C. Schnörr, contributing valuable insights to the field of image and pattern analysis.

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Primal-Dual Iterations for Efficient Solutions of Nonsmooth Variational Problems

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  1. Primal-Dual Iterations for Variational Problems • Convex Saddle-Point formulation is powerful • Nonsmooth variational problems • Relaxed combinatorial problems • DMDR Optimization • 10 times faster • Numerically robust J. Lellmann, D. Breitenreicher, C. Schnörr Image and Pattern Analysis & HCI, University of Heidelberg

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