Primal-Dual Extragradient Methods for Nonlinear Nonsmooth PDE-Constrained Optimization
KAUST Grant NumberKUK-I1-007-43
Permanent link to this recordhttp://hdl.handle.net/10754/668528
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AbstractWe study the extension of the Chambolle-Pock primal-dual algorithm to nonsmooth optimization problems involving nonlinear operators between function spaces. Local convergence is shown under technical conditions including metric regularity of the corresponding primal-dual optimality conditions. We also show convergence for a Nesterov-type accelerated variant, provided one part of the functional is strongly convex. We show the applicability of the accelerated algorithm to examples of inverse problems with L1 and L∞ fitting terms as well as of state-constrained optimal control problems, where convergence can be guaranteed after introducing an (arbitrarily small, still nonsmooth) Moreau-Yosida regularization. This is verified in numerical examples.
CitationClason, C., & Valkonen, T. (2017). Primal-Dual Extragradient Methods for Nonlinear Nonsmooth PDE-Constrained Optimization. SIAM Journal on Optimization, 27(3), 1314–1339. doi:10.1137/16m1080859
SponsorsWhile TV was in Cambridge, he was supported by the King Abdullah University of Science and Technology (KAUST) award KUK-I1-007-43, and EPSRC grants EP/J009539/1 “Sparse & Higher-order Image Restoration” and EP/M00483X/1 “Efficient Computational Tools for Inverse Imaging Problems.” Part of this work was also done while TV was in Quito, where he was supported by a Prometeo scholarship of the Senescyt (Ecuadorian Ministry of Science, Technology, Education, and Innovation). CC is supported by the German Science Foundation DFG under grant Cl 487/1-1.
JournalSIAM Journal on Optimization