Parties d'ouvrages collectifs (2)
  1. 1. Fest, J.-B., Heikkilä, T., Loris, I., Martin, S., Ratti, L., Rebegoldi, S., & Sarnighausen, G. (2024). On a fixed-point continuation method for a convex optimization problem. In Advanced Techniques in Optimization for Machine learning and Imaging (ATOMI 2022) (pp. 15-30). Singapore: Springer.(Springer INdAM Series).
  2. 2. Loris, I. (2014). Numerical algorithms for non-smooth optimization applicable to seismic recovery. In W. Freeden, M. Z. Nashed, & T. Sonar (Eds.), Handbook of Geomathematics (2 ed., pp. 1-33). Springer. doi:10.1007/978-3-642-54551-1_65
    3.   Articles dans des revues avec comité de lecture (44)
  3. 1. Loris, I., & Rebegoldi, S. (2024). Convergence analysis of a primal-dual optimization-by-continuation algorithm. Journal of computational and applied mathematics, 452, 116299. doi:10.1016/j.cam.2024.116299
  4. 2. Chen, J., & Loris, I. (2019). On starting and stopping criteria for nested primal-dual iterations. Numerical algorithms, 82(2), 605-621. doi:10.1007/s11075-018-0616-x
  5. 3. Bonettini, S., Loris, I., Porta, F., Prato, M., & Rebegoldi, S. (2017). On the convergence of a linesearch based proximal-gradient method for nonconvex optimization. Inverse problems, 33(5), 055005. doi:10.1088/1361-6420/aa5bfd
  6. 4. Bonettini, S., Loris, I., Porta, F., & Prato, M. (2016). Variable metric inexact line-search based methods for nonsmooth optimization. SIAM journal on optimization, 26(2), 891-921. doi:10.1137/15M1019325
  7. 5. Prato, M., Bonettini, S., Loris, I., Porta, F., & Rebegoldi, S. (2016). On the constrained minimization of smooth Kurdyka-Łojasiewicz functions with the scaled gradient projection method. Journal of physics. Conference series, 756(1), 012001. doi:10.1088/1742-6596/756/1/012001
  8. 6. Porta, F., & Loris, I. (2015). On some steplength approaches for proximal algorithms. Applied mathematics and computation, 253, 345-362. doi:10.1016/j.amc.2014.12.079
  9. 7. Nassiri, V., & Loris, I. (2014). An efficient algorithm for structured sparse quantile regression. Computational statistics, 29(5), 1321-1343. doi:10.1007/s00180-014-0494-1
  10. 8. Nassiri, V., & Loris, I. (2013). A generalized quantile regression model. Journal of applied statistics, 40(5), 1090-1105. doi:10.1080/02664763.2013.780158
  11. 9. Loris, I., & Verhoeven, C. (2013). An iterative algorithm for sparse and constrained recovery with applications to divergence-free current reconstructions in magneto-encephalography. Computational optimization and applications, 54(2), 399-416. doi:10.1007/s10589-012-9482-y
  12. 10. Charléty, J., Voronin, S., Nolet, G., Loris, I., Simons, F., Sigloch, K., & Daubechies, I. (2013). Global seismic tomography with a sparsity constraint: comparison with smoothing and damping regularization. Journal of Geophysical Research (Solid Earth), 118, 4887-4899. doi:10.1002/jgrb.50326
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