2024

• S. Hundrieser, T. Staudt, and A. Munk. Empirical Optimal Transport between Different Measures Adapts to Lower Complexity, Ann. Inst. Henri Poincaré Probab. Stat. 60(2) (2024), 824-846.

• S. Hundrieser, B. Eltzner, and S. Huckemann. Finite Sample Smeariness of Fréchet Means and Application to Climate, Electron. J. Statist. 18(2) (2024), 3274-3309.

• S. Hundrieser, M. Klatt, A. Munk, and T. Staudt. A Unifying Approach to Distributional Limits for Empirical Optimal Transport, Bernoulli 30(4) (2024), 2846-2877.

• S. Hundrieser, M. Klatt, and A. Munk. Limit Distributions and Sensitivity Analysis for Empirical Entropic Optimal Transport on Countable Spaces, Ann. Appl. Probab. 34(1B) (2024), 1403-1468.

• S. Hundrieser, G. Mordant, C. Weitkamp, and A. Munk. Empirical Optimal Transport under Estimated Costs: Distributional Limits and Statistical Applications, Stochastic Process. Appl. 178 (2024), 104462.

• J. Lueg, M.K. Garba, T.M.W. Nye and S. Huckemann. Foundations of the Wald Space for Phylogenetic Trees, Proc. Lond. Math. Soc. (3) 109(5) (2024), e12893.

• R. Müller, D. Schuhmacher, and J. Mateu. ANOVA for Data in Metric Spaces, with Applications to Spatial Point Patterns, Statist. Sci. 39(2) (2024), 262-285.

• R. Razavi, G. Plonka, and H. Rabbani. X-let's Atom Combinations for Modeling and Denoising of OCT Images by Modified Morphological Component Analysis, IEEE Trans Med Imaging 43(2) (2024), 760-770.

• L. J. Vanegas, B. Eltzner, D. Rudolf, M. Dura, E. Lehnart, and A. Munk. Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications, Ann. Appl. Stat. 18(2) (2024), 1445-1470.

2023

• E. Cohen, R. Luke, T. Pinta, S. Sabach, and M. Teboulle. A semi-Bregman proximal alternating method for a class of nonconvex problems: local and global convergence analysis, J. Global Optim. (2023).

• N. Derevianko, G. Plonka, and M. Petz. From ESPRIT to ESPIRA: estimation of signal parameters by iterative rational approximation. IMA J. Numer. Anal. 43(2) (2023), 789-827.

• N. Derevianko, G. Plonka, and R. Razavi. ESPRIT versus ESPIRA for reconstruction of short cosine sums and its application, Numer. Algorithms 92 (2023), 437-470.

• F. Heinemann, M. Klatt, and A. Munk. Kantorovich-Rubinstein distance and barycenter for finitely supported measures: Foundations and Algorithms, Appl. Math. Optim. 87 (2023), article number:4.

• N. Hermer, R. Luke, and A. Sturm. Rates of Convergence for Chains of Expansive Markov Operators, Trans. Math. Appl. 7(1) (2023), tnad001.

• N. Hermer, R. Luke, and A. Sturm. Nonexpansive Markov Operators and Random Function Iterations for Stochastic Fixed Point Problems, J. Convex Anal. 30(4) (2023), 1073-1114.

• Y. Kolomoitsev, T. Lomako, and S. Tikhonov. Sparse grid approximation in weighted Wiener spaces, J. Fourier Anal. Appl. 29 (2023), article number:19.

• Y. Kolomoitsev and T. Lomako. On generalized K-functionals in Lp for 0 < p < 1, Fract. Calc. Appl. Anal. 26 (2023), 1016-1030.

• Y. Kolomoitsev and T. Lomako. Sharp Lp-error estimates for sampling operators, J. Approx. Theory 294 (2023), 105941.

• F. Memoli, A. Munk, Z. Wan, and C. Weitkamp. The ultrametric Gromov-Wasserstein distance, Discrete Comput. Geom. 70 (2023), 1378-1450.

• R. Müller, A. Schöbel, and D. Schuhmacher. Location Problems with Cutoff, Asia-Pac. J. Oper. Res. 40(3) (2023), 2250045.

• G. Plonka, Y. N. Riebe, and Y. Kolomoitsev. Spline Representation and Redundancies of One-Dimensional ReLU Neural Network Models. Anal. Appl. (Singap.) 21(1) (2023), 127-163.

• M. Pohlmann, F. Werner, and A. Munk. Minimax detection of localized signals in statistical inverse problems, Inf. Inference 12(3) (2023), 2160-2196.

• F. Telschow, M. Pierrynowski, and S. Huckemann. Confidence Tubes for Curves on SO(3) and Identification of Subject-Specific Gait Change after Kneeling, J. R. Stat. Soc. Ser. C. Appl. Stat. 72(5) (2023), 1354-1374.

2022

• A. Berdellima, F. Lauster, and R. Luke. α-Firmly nonexpansive operators on metric spaces. J. Fixed Point Theory Appl. 24 (2022), article number:14.

• N. Derevianko and G. Plonka. Exact reconstruction of extended exponential sums using rational approximation of their Fourier coefficients. Anal. Appl. (Singap.) 20(3) (2022), 543-577.

• B. Eltzner. Geometrical Smeariness -- A new Phenomenon of Fréchet Means. Bernoulli 28(1) (2022), 239-254.

• J. Geppert and G. Plonka. Frame Soft Shrinkage Operators are Proximity Operators. Appl. Comput. Harmon. Anal. 57 (2022), 185-200.

• F. Heinemann, A. Munk, and Y. Zemel. Randomised Wasserstein Barycenter Computation: Resampling with Statistical Guarantees. SIAM J. Math. Data Sci. 4(1) (2022), 229-259.

• M. Klatt, A. Munk, and Y. Zemel. Limit laws for empirical optimal solutions in random linear programs. Ann. Oper. Res. 315 (2022), 251-278.

• Y. Kolomoitsev. Approximation by quasi-interpolation operators and Smolyak's algorithm. J. Complexity 69 (2022), 11601.

• Y. Kolomoitsev and M. Skopina. Uniform approximation by multivariate quasi-projection operators. Anal. Math. Phys. 12(68) (2022).

• P. Miller and T. Hohage. Convergence rates for oversmoothing Banach space regularization. ETNA 57 (2022), 101-126.

• L. J. Vanegas, M. Behr, and A. Munk. Multiscale quantile segmentation. J. Amer. Statist. Assoc. 117(539) (2022), 1384-1397.

• C. Weitkamp K. Proksch, C. Tameling, and A. Munk. Distribution of Distances based Object Matching: Asymptotic Inference. J. Amer. Statist. Assoc. 119(545) (2022), 538-551.

2021

• M. del Alamo Ruiz, H. Li, and A. Munk. Frame-constrained Total Variation Regularization for White Noise Regression. Ann. Statist. 49(3) (2021), 1318-1346.

• B. Eltzner, F. Galaz-Garcia, S. Huckemann, and W. Tuschmann. Stability of the Cut Locus and a Central Limit Theorem for Fréchet Means of Riemannian Manifolds. Proc. Amer. Math. Soc. 149 (2021), 3947-3963.

• M.K. Garba, T.M.W. Nye, J. Lueg, and S. Huckemann. Information geometry for phylogenetic trees. J. Math. Biol. 82 (2021), article number 19.

• H. Knirsch, M. Petz, and G. Plonka. Optimal Rank-1 Hankel Approximation of Matrices: Frobenius Norm, Spectral Norm and Cadzow's Algorithm. Linear Algebra Appl. 629 (2021), 1-39.

• G. Kulaitis, A. Munk, and F. Werner. What is resolution? A statistical minimax testing perspective on superresolution microscopy. Ann. Statist. 49(4) (2021), 2292-2312.

• P. Miller. Variational Regularization Theory Based on Image Space Approximation Rates. Inverse Problems 37(6) (2021), 065003.

• P. Miller and T. Hohage. Maximal Spaces for Approximation Rates in ℓ1-regularization. Numer. Math. 149 (2021), 341-374.

• V. Natarovskii, D. Rudolf, and B. Sprungk. Quantitative spectral gap estimate and Wasserstein contraction of simple slice sampling. Ann. Appl. Probab. 31(2) (2021), 806-825.

• M. Pelizzola, M. Behr, H. Li, A. Munk, and A. Futschik. Multiple haplotype reconstruction from allele frequency data. Nat. Comput. Sci. 1 (2021), 262-271.

• M. Petz, G. Plonka, and N. Derevianko. Exact Reconstruction of Sparse Non-Harmonic Signals from their Fourier Coefficients. Sampl. Theory Signal Image Process. 19(7) (2021), article number: 7.

• G. Plonka and T. von Wulffen. Deterministic Sparse Sublinear FFT with Improved Numerical Stability. Results Math. 76 (2021), 53.

• R. Richter, D. H. Thai, and S. Huckemann. Generalized Intersection Algorithms with Fixed Points for Image Decomposition Learning. SIAM J. Imaging Sci. 14(3) (2021), 1273–1305.

• J. Schmidt-Hieber, L. F. Schneider, T. Staudt, A. Krajina, T. Aspelmeier, and A. Munk. Posterior analysis of n in the binomial (n,p) problem with both parameters unknown—with applications to quantitative nanoscopy. Ann. Statist. 49(6). (2021), 3534-3558.

• L. F. Schneider, A. Krajina, and T. Krivobokova. Threshold Selection in Univariate Extreme Value Analysis. Extremes 24 (2021), 881-913.

• C. Tameling, S. Stoldt, T. Stephan, J. Naas, S. Jakobs, and A. Munk. Colocalization for super-resolution microscopy via optimal transport. Nat. Comput. Sci. 1 (2021), 199-211.

• J. Wieditz, Y. Pokern, D. Schuhmacher, and S. Huckemann. Characteristic and necessary minutiae in fingerprints. J. R. Stat. Soc. Series C 71(1) (2021), 27-50.

2020

• M. del Alamo, H. Li, A. Munk, and F. Werner. Variational Multiscale Nonparametric Regression: Algorithms and Implementation. Algorithms 13(11) (2020), 296.

• M. del Alamo Ruiz and A. Munk. Total variation multiscale estimators for linear inverse problems. Inf. Inference 9(4) (2020), 961-986.

• S. Behrends and A. Schöbel. Generating Valid Linear Inequalities for Nonlinear Programs via Sums of Squares. J. Nonlinear Anal. Optim. 186 (2020), 911-935.

• M. Botte. Fixed gate point location problems. TOP (2020).

• R. Budinich and G. Plonka. A tree-based dictionary learning framework. Int. J. Wavelets Multiresolut. Inf. Process. 18(5) (2020).

• F. Enikeeva, A. Munk, M. Pohlmann, and F. Werner. Bump detection in the presence of dependency: Does it ease or does it load? Bernoulli 26(4) (2020), 3280-3310.

• M. Klatt, C. Tameling, and A. Munk. Empirical Regularized Optimal Transport: Statistical Theory and Applications, SIAM J. Math. Data Sci. 2(2) (2020), 419-443.

• D. R. Luke and A.-L. Martins. Convergence Analysis of the Relaxed Douglas-Rachford Algorithm, SIAM J. Optim. 30(1) (2020), 542–584.

• D. R. Luke, M. Teboulle, and N. H. Thao. Necessary conditions for linear convergence of iterated expansive, set-valued mappings. Math. Program. A 180 (2020), 1-31.

• R. Müller, D. Schuhmacher, and J. Mateu. Metrics and barycenters for point pattern data. Stat. Comput. 30(4) (2020), 953-972.

• D. Rudolf and B. Sprungk. On a Metropolis-Hastings importance sampling estimator, Electron. J. Stat. 14(1) (2020), 857-889.

• C. Schillings, B. Sprungk, and P. Wacker. On the Convergence of the Laplace Approximation and Noise-Level-Robustness of Laplace-based Monte Carlo Methods for Bayesian Inverse Problems, Numer. Math. 145(4) (2020), 915-971.

• C. Shi, C. Ropers, and T. Hohage. Density Matrix Reconstructions in Ultrafast Transmission Electron Microscopy: Uniqueness, Stability, and Convergence Rates, Inverse Problems 36(2) (2020), 025005.

• B. Sprungk. On the Locally Lipschitz Robustness of Bayesian Inverse Problems, Inverse Problems 36(5) (2020), 055015-55046.

• K. Stampfer and G. Plonka. The Generalized Operator Based Prony Method. Constr. Approx. 52. (2020), 247–282.

• T. Staudt, T. Aspelmeier, O. Laitenberger, C. Geisler, A. Egner, and A. Munk. Statistical Molecule Counting in Super-Resolution Fluorescence Microscopy: Towards Quantitative Nanoscopy, Statist. Sci. 35(1) (2020), 92-111.

• F. Weidling, B. Sprung, and T. Hohage. Optimal Convergence Rates for Tikhonov Regularization in Besov Spaces, SIAM J. Numer. Anal. 58(1) (2020), 21-47.

2019

• A. Berdellima. A Note on a Conjecture by Khabibullin. J. Math. Sci. (N.Y.) 243(6) (2019), 825-834.

• S. Bittens and G. Plonka. Real Sparse Fast DCT for Vectors with Short Support, Linear Algebra Appl. 582 (2019), 359-390.

• S. Bittens and G. Plonka. Sparse fast DCT for vectors with one-block support. Numer. Algorithms 82(2) (2019), 663-697.

• S. Bittens, R. Zhang, and M. Iwen. A deterministic sparse FFT for functions with structured Fourier sparsity. Adv. Comput. Math. 45(2) (2019), 519-561.

• M. Botte and A. Schöbel. Dominance for multi-objective robust optimization concepts. European J. Oper. Res. 35(2) (2019), 430-440.

• M. N. Dao and M. K. Tam. A Lyapunov-type approach to convergence of the Douglas–Rachford algorithm for a nonconvex setting. J. Global Optim. 73(1) (2019), 83-112.

• M. N. Dao and M. K. Tam. Union Averaged Operators with Applications to Proximal Algorithms for Min-Convex Functions. J. Optim. Theory Appl. 181(1) (2019), 61-94.

• J. Eckhardt, R. Hiptmair, T. Hohage, H. Schumacher, and M. Wardetzky. Elastic energy regularization for inverse obstacle scattering problems. Inverse Problems 35(10) (2019), 104009.

• G. Eichfelder, T. Hotz, and J. Wieditz. An algorithm for computing Fréchet means on the sphere. Optim. Lett. 13(7) (2019), 1523-1533.

• L. Frahm, K. Keller-Findeisen, P. Alt, S. Schnorrenberg, M. del Alamo Ruiz, T. Aspelmeier, A. Munk, S. Jakobs, and S. Hell. The molecular contribution function in RESOLFT nanoscopy. Optics Express 27(15) (2019), 21956-21987.

• J. Geppert, F. Krahmer, and D. Stöger. Sparse Power Factorization: Balancing peakiness and sample complexity. Adv. Comput. Math. 45(3) (2019), 1711–1728.

• N. Hermer, D. R. Luke, and A. Sturm. Random Function Iterations for Consistent Stochastic Feasibility. Numer. Funct. Anal. Optim. 40(4) (2019), 386-420.

• T. Hohage and P. Miller. Optimal convergence rates for sparsity promoting wavelet-regularization in Besov spaces. Inverse Problems 35(6) (2019), 065005.

• H. Li, Q. Guo, and A. Munk. Multiscale change-point segmentation: beyond step functions. Electron. J. Stat. 13(2) (2019), 3254-3296.

• K. Markert, K. Krehl, G. Gottschlich, and S. F. Huckemann. Detecting Anisotropy in Fingerprint Growth, J. R. Stat. Soc. Ser. C. Appl. Stat. 68(4) (2019), 1007-1027.

• G. Plonka and V. Pototskaia. Computation of Adaptive Fourier Series by Sparse Approximation of Exponential Sums. J. Fourier Anal. Appl. 25(4) (2019), 1580–1608.

• G. Plonka, K. Stampfer, and I. Keller. Reconstruction of stationary and non-stationary signals by the generalized Prony method. Anal. Appl. 17(2) (2019), 179-210.

• R. Richter, C. Gottschlich, L. Mentch, D. H. Thai, and S. F. Huckemann. Smudge Noise for Quality Estimation of Fingerprints and its Validation. IEEE Trans. on Information Forensics and Security 14(8) (2019), 1963-1974.

• M. Sommerfeld, J. Schrieber, Y. Zemel, and A. Munk. Optimal Transport: Fast Probabilistic Approximation with Exact Solvers. Journ. Mach. Learn. Research 20(105) (2019), 1-23.

• B. Sprung. Upper and lower bounds for the Bregman divergence. J. Inequal. Appl. 2019:4 (2019).

• B. Sprung and T. Hohage. Higher order convergence rates for Bregman iterated variational regularization of inverse problems. Numer. Math. 141(1) (2019), 215–252.

• C. Tameling, M. Sommerfeld and A. Munk. Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications. Ann. Appl. Probab. 29(5) (2019), 2744-2781.

• R. Zhang and G. Plonka. Optimal approximation with exponential sums by a maximum likelihood modification of Prony's method. Adv. Comput. Math. 45(3) (2019), 1657–1687.

2018

• F. J. Aragon Artacho, R. Campoy, I. Kotsireas, and M.K. Tam. A feasibility approach for constructing combinatorial designs of circulant type. J. Comb. Optim. 35(4) (2018), 1061-1085.

• M. Behr, C. Holmes, and A. Munk. Multiscale blind source separation. Ann. Statist. 46 (2018), 711-744.

• S. Behrends, R. Hübner, and A. Schöbel. Norm bounds and underestimators for unconstrained polynomial integer minimization. Math. Methods Oper. Res. 87(1) (2018), 73–107.

• R. Beinert and G. Plonka. Enforcing uniqueness in one-dimensional phase retrieval by additional signal information in time domain. Appl. Comput. Harmon. Anal. 45(3) (2018), 505-525.

• A. Berdellima. On a conjecture of Khabibullin about a pair of integral inequalities. Ufa Math. J. 10(3) (2018), 117-130.

• C. Clason, C. Tameling, and B. Wirth. Vector-Valued Multibang Control of Differential Equations. SIAM J. Control Optim. 56(3) (2018), 2295-2326.

• O. G. Ernst, B. Sprungk, and L. Tamellini. Convergence of Sparse Collocation for Functions of Countably Many Gaussian Random Variables (with Application to Elliptic PDEs). SIAM J. Numer. Anal. 56(2) (2018), 877-905.

• M. Grasmair, H. Li, A. Munk. Variational Multiscale Nonparametric Regression: Smooth Functions. Ann. Inst. H. Poincare Probab. Statist. 54(2) (2018), 1058-1097.

• A. Y. Kruger, D. R. Luke, and N. H. Thao. Set regularities and feasibility problems. Math. Program. B 168(1-2) (2018), 279–311.

• F. Lauster, D. R. Luke, and M. K. Tam. Symbolic Computation with Monotone Operators. Set-Valued Var. Anal. 26 (2018), 353–368.

• D. R. Luke, N. H. Thao, and M. K. Tam. Implicit Error Bounds for Picard Iterations on Hilbert Spaces. Vietnam J. Math. 46(2) (2018), 243-258.

• D. R. Luke, N. H. Thao, and M. K. Tam. Quantitative convergence analysis of iterated expansive, set-valued mappings. Math. Oper. Res. 43(4) (2018) 1051-1404.

• G. Plonka, K. Wannenwetsch, A. Cuyt, and W.-s. Lee. Deterministic Sparse FFT for M-sparse Vectors. Numer. Algorithms 78(1) (2018), 133-159.

• D. Rudolf and B. Sprungk. On a Generalization of the Preconditioned Crank–Nicolson Metropolis Algorithm. Found. Comput. Math. 18(2) (2018), 309-343.

• M. Sommerfeld and A. Munk. Inference for empirical Wasserstein distances on finite spaces. J. R. Stat. Soc. Ser. B Stat. Methodol. 80(1) (2018), 219-238.

• M. Sommerfeld, S. Sain, and A. Schwartzman. Confidence regions for excursion sets in asymptotically Gaussian random fields, with an application to climate. J. Amer. Statist. Assoc. 113(523) (2018), 1327-1340.

• D. Stöger, J. Geppert, and F. Krahmer. Sparse power factorization with refined peakiness conditions. 2018 IEEE Statistical Signal Processing Workshop (SSP) (2018), 816-820.

• N. H. Thao. A convergent relaxation of the Douglas–Rachford algorithm. Comput. Optim. Appl. 70(3) (2018), 841-863.

2017

• H. Ammari, F. Romero, and C. Shi. A Signal Separation Technique for Sub-Cellular Imaging Using Dynamic Optical Coherence Tomography. Multiscale Model. Simul. 15(3) (2017), 1155-1175.

• M. Behr and A. Munk. Identifiability for Blind Source Separation of Multiple Finite Alphabet Linear Mixtures. IEEE Trans. Inf. Theory 63 (2017), 5506-5517.

• R. Beinert. Ambiguities in one-dimensional phase retrieval from magnitudes of a linear canonical transform. ZAMM Z. Angew. Math. Mech. 97(9) (2017), 1078-1082.

• R. Beinert. Non-negativity constraints in the one-dimensional discrete-time phase retrieval problem. Inf. Inference 6(2) (2017), 213–224.

• R. Beinert. One-dimensional phase retrieval with additional interference measurements. Results Math. 72(1-2) (2017), 1-24.

• S. Bittens. Sparse FFT for Functions with Short Frequency Support. Dolomites Res. Notes Approx. 10 (2017), 43-55.

• J. M. Borwein, G. Li, and M. K. Tam. Convergence Rate Analysis for Averaged Fixed Point Iterations in Common Fixed Point Problems. SIAM J. Optim. 427 (2017), 1-33.

• R. Budinich. A region based easy path wavelet transform for sparse image representation. Int. J. Wavelets Multiresolut. Inf. Process. 15 (2017), 1750045 [23 pages].

• P. Elbau, O. Scherzer, and C. Shi. Singular values of the attenuated photoacoustic imaging operator. J. Differential Equations 263(9) (2017), 5330-5376.

• J. Geppert, F. Krahmer, and D. Stöger. Refined performance guarantees for Sparse Power Factorization. Sampling Theory and Applications (SampTA) 2017 International Conference on. 509-513.

• A. Y. Kruger, D. R. Luke, and N. H. Thao. About Subtransversality of Collections of Sets. Set-Valued Var. Anal. 25(4) (2017), 701-729.

• G. Plonka and K. Wannenwetsch. A sparse Fast Fourier Algorithm for Real Nonnegative Vectors. J. Comput. Appl. Math. 321 (2017), 532-539.

• J. Schrieber, D. Schuhmacher, and C. Gottschlich. DOTmark – A Benchmark for Discrete Optimal Transport. IEEE ACCESS 5 (2017), 271-282.

• M. Sommerfeld, G. Heo, P. Kim, S. T. Rush, and J. S. Marron. Bump hunting by topological data analysis. Stat. 6(1) (2017), 462-471.

• M. K. Tam. Regularity properties of non-negative sparsity sets. J. Math. Anal. Appl. 447 (2017), 758-777.

2016

• S. Huckemann, K.-R. Kim, A. Munk, F. Rehfeldt, M. Sommerfeld, J. Weickert, and C. Wollnik. The circular SiZer, inferred persistence of shape parameters and application to early stem cell differentiation. Bernoulli 22 (2016), 2113-2142.

• A. Y. Kruger and N. H. Thao. Regularity of collections of sets and convergence of inexact alternating projections. J. Convex Anal. 23 (2016), 823-847.

• H. Li, A. Munk, and H. Sieling. FDR-control in multiscale change-point segmentation. Electron. J. Stat. 10 (2016), 918-959.

• G. Plonka and K. Wannenwetsch. A deterministic sparse FFT algorithm for vectors with small support. Numer. Algorithms 71(4) (2016), 889-905.

2015

• R. Beinert and G. Plonka. Ambiguities in one-dimensional discrete phase retrieval from Fourier magnitudes. J. Fourier Anal. Appl. 21 (2015), 1169-1198.

• D. James and H. Rauhut. Nonuniform sparse recovery with random convolutions. Sampling Theory and Applications (SampTA) 1 (2015), 34-38.

• J.S. Jørgensen, C. Kruschel, and D. A. Lorenz. Testable uniqueness conditions for empirical assessment of undersampling levels in total variation-regularized X-ray CT. Inverse Probl. Sci. Eng. 23 (2015), 1283-1305.

• P. Q. Khan, A. Y. Kruger, and N. H. Thao. An Induction Theorem and Nonlinear Regularity Models. SIAM J. Optim. 25 (2015), 2561-2588.

• A. Y. Kruger and N. H. Thao. Quantitative Characterizations of Regularity Properties of Collections of Sets. J. Optim. Theory Appl. 164 (2015), 41-67.

• C. Kruschel and D. A. Lorenz. Computing and analyzing recoverable supports for sparse reconstruction. Adv. Comput. Math. 41(6) (2015), 1119-1144.

• P. Peter, J. Weickert, A. Munk, T. Krivobokova, and H. Li. Justifying Tensor-Driven Diffusion from Structure-Adaptive Statistics of Natural Images. Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science 8932 (2015), 263-277.