[24]: M. Sabaté Landman. Flexible inner-product free Krylov methods for inverse problems. ArXiv, 2025.

[23]: M. Sabaté Landman, Y. Nakatsukasa. Randomized flexible Krylov methods for ℓp reg. ArXiv, 2025.

[22]: S. Gazzola, M. Sabaté Landman. Flexible and inexact Golub-Kahan for inverse problems ArXiv, 2025.

[21]: F. Vickers Hastings, S M R. S. Islam, M. Sabaté Landman,  S.Hatamikia, C. Schönlieb, A. Biguri. Real-time CBCT Reconstructions Using Krylov Solvers in Repeated Scanning Procedures. ArXiv, 2025.

[20]: L. Onisk, M. Sabaté Landman. Iterative Refinement and Flexible Iteratively Reweighed Solvers for Linear Inverse Problems with Sparse Solutions. ArXiv, 2025.

[19]: M. Sabaté Landman, A. Brown, J. Chung, J. Nagy. Randomized and Inner-product Free Krylov Methods for Large-scale Inverse Problems. Numer. Algor., 2025. doi = '10.1007/s11075-025-02167-w'

[18]: A. Biguri, T. Sadakane, R. Lindroos, Y. Liu, M. Sabaté Landman, et al. TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets. Eng. Res. Express. 2025.

[17] F. van Maarschalkerwaart, M. Sabaté Landman, S. Mukherjee, C. Brune, M. Carioni. Perturbations-Aware Distributionally Robust Optimization for Inverse Problems. Conference proceedings - submitted, 2024.

[16]:  Efficient hyperparameter estimation in Bayesian inverse problems using sample average approximation. J. Chung, S. M. Miller, M. Sabaté Landman, and A. K. Saibaba. Philos. Trans. R. Soc. A, 2025.

[15]:  Inner product free Krylov methods for large-scale inverse problems. A. Brown, J. Chung, J. Nagy, M. Sabaté Landman. Accepted in SIAM J. Sci. Comput., 2025.

[14]:  A Joint Reconstruction and Model Selection Approach for Large Scale Inverse Modeling.  M. Sabaté Landman, J. Chung, J. Jiang, S. Miller, A. Saibaba. Geosci. Model Dev. 2024. doi = '10.5194/gmd-2024-90'.

[13]:  A study of why we need to reassess full reference image quality assessment with medical images.  A. Breger, A. Biguri, M. Sabaté Landman, I. Selby, et al. J Digit Imaging. Inform. med. 2024. doi = '10.1007/s10278-025-01462-1' 

[12]:  H-CMRH: an inner product free hybrid Krylov method for large-scale inverse problems. A. Brown, M. Sabaté Landman, J. G. Nagy.  SIAM J. Matrix Anal. Appl. 2024. doi = '10.1137/24M1634874'.'

[11]: Optimal sparse energy sampling for X-ray spectro-microscopy: Reducing X-ray dose and experiment time using model order reduction.  P. D. Quinn, M. Sabate Landman, T. Davis, M. Freitag, S. Gazzola, S. Dolgov. Chemical & Biomedical Imaging, 2024.

[10]: Augmented Flexible Krylov Subspace Methods with Applications to Bayesian Inverse Problems. M. Sabaté Landman, J. Jiang, J. Zhang and W. Ren. Linear Algebra and its Applications, 2024. doi = ’10.1016/j.laa.2024.05.007’

[9]: Flexible Krylov Methods for Group Sparsity Regularization. M. Sabaté Landman, J. Chung. Accepted for publication in Physica Scripta, 2024. doi = '10.48550/arXiv.2306.08499'

[8]: On Krylov Methods for Large Scale CBCT Reconstruction. M. Sabaté Landman, A. Biguri, S. Hatamikia, R. Boardman, J. Aston, C-B. Schonlieb. Physics in Medicine & Biology, 2023. doi = '10.1088/1361-6560/acd616'

[7]: Nonlinear motion separation via untrained generator networks with disentangled latent space variables and applications to cardiac MRI. A. Abdullah, M. Holler, K. Kunisch and M. Sabate Landman. International Conference on Scale Space and Variational Methods in Computer Vision, 2023. doi = ’10.48550/arXiv.2205.10367’

[6]: Regularization by inexact Krylov methods with applications to blind deblurring. S. Gazzola and M. Sabate Landman. SIAM J. Matrix Anal. Appl., 2021. doi = ’10.1137/21M1402066’

[5]: Autonomous Exploration and Identification of High Performing Adsorbents using Active Learning. G. Donval, C. Hand, J. Hook, E. Dupont, M. Sabate Landman, M. A. Freitag, M. J. Lennox, and T. Düren.  pre-print, 2020. doi =’10.26434/chemrxiv.14555706’

[4]: Iteratively reweighted FGMRES and FLSQR for Sparse Reconstruction. S. Gazzola, M. Sabate Landman and J. Nagy.  SIAM J. Sci. Comput., 2020. doi=’10.1137/20M1333948’

[3]: Krylov Methods for Inverse Problems: surveying classical, and introducing new, algorithmic approaches. 

S. Gazzola, M. Sabate Landman. GAMM-Mitteilungen, 2020. doi=’10.1002/gamm.202000017’

[2]: Flexible GMRES for Total Variation regularization. S. Gazzola, M. Sabate Landman. BIT Numerical Mathematics, 2019. doi = ’10.1007/s10543-019-00750-x’

[1]: The Deterioration of the Spanish Youth Labour Market (1985–2015): An Interdisciplinary Case Study.  M. Úbeda Pavia, T. Strecker, M. À. Piqué and M. Sabate Landman.  YOUNG, 2020. doi=’10.1177/1103308820914838