References in zbMATH (referenced in 698 articles )

Showing results 21 to 40 of 698.
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  1. Gebken, Bennet; Peitz, Sebastian: An efficient descent method for locally Lipschitz multiobjective optimization problems (2021)
  2. Genzel, Martin; Kutyniok, Gitta; März, Maximilian: (\ell^1)-analysis minimization and generalized (co-)sparsity: when does recovery succeed? (2021)
  3. Huang, Jian; Jiao, Yuling; Jin, Bangti; Liu, Jin; Lu, Xiliang; Yang, Can: A unified primal dual active set algorithm for nonconvex sparse recovery (2021)
  4. Huang, Yong; Beck, James L.; Li, Hui; Ren, Yulong: Sequential sparse Bayesian learning with applications to system identification for damage assessment and recursive reconstruction of image sequences (2021)
  5. Jiang, Shan; Fang, Shu-Cherng; Jin, Qingwei: Sparse solutions by a quadratically constrained (\ellq) ((0 < q< 1)) minimization model (2021)
  6. Ke, Chengyu; Ahn, Miju; Shin, Sunyoung; Lou, Yifei: Iteratively reweighted group Lasso based on log-composite regularization (2021)
  7. Li, Peixuan; Shen, Yuan; Jiang, Suhong; Liu, Zehua; Chen, Caihua: Convergence study on strictly contractive peaceman-Rachford splitting method for nonseparable convex minimization models with quadratic coupling terms (2021)
  8. Luu, Tung Duy; Fadili, Jalal; Chesneau, Christophe: Sampling from non-smooth distributions through Langevin diffusion (2021)
  9. Shang, Pan; Kong, Lingchen: Regularization parameter selection for the low rank matrix recovery (2021)
  10. Van Hieu, Dang; Anh, Pham Ky; Muu, Le Dung: Modified forward-backward splitting method for variational inclusions (2021)
  11. Wang, Wei; Xia, Xiang-Gen; He, Chuanjiang; Ren, Zemin; Wang, Tianfu; Lei, Baiying: A noise-robust online convolutional coding model and its applications to Poisson denoising and image fusion (2021)
  12. Wang, Wendong; Zhang, Feng; Wang, Jianjun: Low-rank matrix recovery via regularized nuclear norm minimization (2021)
  13. Xiong, Meixin; Chen, Liuhong; Ming, Ju; Shin, Jaemin: Accelerating the Bayesian inference of inverse problems by using data-driven compressive sensing method based on proper orthogonal decomposition (2021)
  14. Zeng, Liaoyuan; Yu, Peiran; Pong, Ting Kei: Analysis and algorithms for some compressed sensing models based on L1/L2 minimization (2021)
  15. Zhang, Jian-Jun; Ye, Wan-Zhou: A modulus-based iterative method for sparse signal recovery (2021)
  16. Akkaya, Deniz; Pınar, Mustafa Ç.: Minimizers of sparsity regularized Huber loss function (2020)
  17. Bastani, Hamsa; Bayati, Mohsen: Online decision making with high-dimensional covariates (2020)
  18. Benedetto, John J.; Li, Weilin: Super-resolution by means of Beurling minimal extrapolation (2020)
  19. Bettiol, Enrico; Létocart, Lucas; Rinaldi, Francesco; Traversi, Emiliano: A conjugate direction based simplicial decomposition framework for solving a specific class of dense convex quadratic programs (2020)
  20. Brauer, Christoph; Lorenz, Dirk A.: Complexity and applications of the homotopy principle for uniformly constrained sparse minimization (2020)

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