low-rank solvernuclear norm regularizationKrylov methodsKronecker productimage problemsflexible Krylov methodsThis paper introduces new solvers for the computation of low-rank approximate solutions to large-scale linear problems, with a particular focus on the regularization of linear inverse problems. ...
Tikhonov regularization based on generalized Krylov subspace methods We consider Tikhonov regularization of large linea Reichel,L.,Sgallari,... - 《Applied Numerical Mathematics Transactions of Imacs》 被引量: 57发表: 2012年 CGLS-GCV: a hybrid algorithm for low-rank-deficient problems Given A=A+...
空间计量经济学:模型、方法和应用Spatial econometrics: models, methods and applications 热度: Tu t o r ia l: Kr y lo v Su b s p a c e Me t h o d s Pe r Chris t ia n Ha ns e n Te chnica l Unive rs it y of De nma rk ...
Another kind of efficiency methods is the Lanczos bi-diagonalization algorithm (Gao et al., 2013). Show abstract Intelligent interpolation by Monte Carlo machine learning 2018, Geophysics GCV for Tikhonov regularization by partial SVD 2017, BIT Numerical Mathematics Efficient matrix completion for ...
3. Galerkin-Based Methods In this section, we will apply the Galerkin projection method to obtain low-rank approximate solutions of the nonsymmetric Stein matrix Equation (1). This approach has been applied for Lyapunov, Sylvester or Riccati matrix equations [1,14,15,19,20,21,23,25,26]. 3....
3. Galerkin-Based Methods In this section, we will apply the Galerkin projection method to obtain low-rank approximate solutions of the nonsymmetric Stein matrix Equation (1). This approach has been applied for Lyapunov, Sylvester or Riccati matrix equations [1,14,15,19–21,23,25,26]. 3.1....