Barlow. A regularized structured total least squares algorithm for high-resolution image reconstruc- tion. Linear Algebra Appl., 391(1):75-98, 2004.H. Fu, J.L. Barlow, A regularized structured total least square
A fast adaptive reweighted residual-feedback iterative algorithm for fractional-order total variation regularized multiplicative noise removal of partly-textured images Some Practice Problems for the C++ Exam and Solutions for the Problems weight annealing heuristics for solving bin packing and other combinat...
aHe proposed solving a regularized least-squares problem in which a scalar parameter determines the tradeoff between fidelity to the data and smoothness of the filtered sequence 他提议解决一个标量参量确定交易在被过滤的序列的保真度到数据和平滑性之间的一个规则化的最小平方的问题 [translate] ...
We introduce a partial proximal point algorithm for solving nuclear norm regularized matrix least squares problems with equality and inequality constraints. The inner subproblems, reformulated as a system of semismooth equations, are solved by an inexact smoothing Newton method, which is proved to be ...
Zhang, Global convergence of a regularized factorized quasi-Newton method for nonlinear least squares problems, Comput. Appl. Math. 29 (2010), pp. 195-214.W.Zhou,L.Zhang.Global convergence of a regularized factorized quasi-Newton method for nonlinear least squares problems. Comput Appl.Math . ...
An efficient Gauss–Newton algorithm for solving regularized total least squares problems Article 18 June 2021 A new structured spectral conjugate gradient method for nonlinear least squares problems Article 23 December 2023 A modified secant equation quasi-Newton method for unconstrained optimization ...
The chip-scale integration of optical spectrometers may offer new opportunities for in situ bio-chemical analysis, remote sensing, and intelligent health care. The miniaturization of integrated spectrometers faces the challenge of an inherent trade-off b
To find the joint feature representation we need to maximize the correlation betweenUandV,or the value of $ \rho $. In the scikit-learn implementation, this is accomplished in the Partial Least Squares algorithm. Kernel CCA is another variant which utilizes aLagrangian-based solution.Gong et al...
network, which uses its full capacity and learns a highly nonlinear function; (right) LR(X) determines a linear regression function that fits to the outputs of FW(X) where the faded dotted line shows FW(X); (middle) FW∗(X) is the same network as FW(X) but regularized by LR(X)...
[13], Clason proposed to use a Moreau-Yosida approximation forL∞-norm constraint and apply a semi-smooth Newton method to solve for the resulting optimality condition. Another possible approach is to consider the dual problem of theL∞, it is aL1regularized least squares problem for which ...