A Continuous Exact $\ell_0$ Penalty (CEL0) for Least Squares Regularized Problem
نویسندگان
چکیده
منابع مشابه
A Continuous Exact ℓ0 Penalty (CEL0) for Least Squares Regularized Problem
Lemma 4.4 in [E. Soubies, L. Blanc-Féraud and G. Aubert, SIAM J. Imaging Sci., 8 (2015), pp. 1607–1639] is wrong for local minimizers of the continuous exact `0 (CEL0) functional. The argument used to conclude the proof of this lemma is not sufficient in the case of local minimizers. In this note, we supply a revision of this lemma where new results are established for local minimizers. Theorem...
متن کاملSuperlinearly convergent exact penalty projected structured Hessian updating schemes for constrained nonlinear least squares: asymptotic analysis
We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of N...
متن کاملsuperlinearly convergent exact penalty projected structured hessian updating schemes for constrained nonlinear least squares: asymptotic analysis
we present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step q-superlinear convergence. the approach is based on an adaptive structured scheme due to mahdavi-amiri and bartels of the exact penalty method of coleman and conn for nonlinearly constrained optimization problems. the structured adaptation also makes use of the ideas of n...
متن کاملRegularized Least-Squares Classification
We consider the solution of binary classification problems via Tikhonov regularization in a Reproducing Kernel Hilbert Space using the square loss, and denote the resulting algorithm Regularized Least-Squares Classification (RLSC). We sketch the historical developments that led to this algorithm, and demonstrate empirically that its performance is equivalent to that of the well-known Support Ve...
متن کاملRegularized Least Squares
{(xi, yi)}i=1 are the given data points, V (·) represents the loss function indicating the penalty we pay for predicting f(xi) when the true value is yi, and ‖f‖H is a Hilbert space norm regularization term with a regularization parameter λ which controls the stability of the solution and trades-off regression accuracy for a function with small norm in RKHS H. Denote by S the training set {(x1,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Imaging Sciences
سال: 2015
ISSN: 1936-4954
DOI: 10.1137/151003714