Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Entropic proximal mappings with applications to nonlinear programming
Mathematics of Operations Research
Proximal Minimization Methods with Generalized Bregman Functions
SIAM Journal on Control and Optimization
The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography
SIAM Journal on Optimization
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Mirror descent and nonlinear projected subgradient methods for convex optimization
Operations Research Letters
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We introduce a first-order Mirror-Descent (MD) type algorithm for solving nondifferentiable convex problems having a combination of simple constraint set X (ball, simplex, etc.) and an additional functional constraint. The method is tuned to exploit the structure of X by employing an appropriate non-Euclidean distance-like function. Convergence results and efficiency estimates are derived. The performance of the algorithm is demonstrated by solving certain image deblurring problems.