Instead of the filter structure, we adopt a nonmonotonic equilibrium mechanism with an adaptive parameter adjusted according to the result of each iteration. Feasibility of the algorithm are given, and the convergence under some assumptions is demonstrated. Numerical results and...
Worst, average, and best-case complexity.Often, not all problem instances require the same amount of resources (space or time) to be solved by the same program/algorithm.Worst case complexityrefers to the cost of the worst (most expensive) instance of the problem when solved by the best pos...
Two-dimensional analysis of an iterative nonlinear optimal control algorithm Nonlinear optimal control problems usually require solutions using iterative procedures and, hence, they fall naturally in the realm of 2-D systems where t... Roberts,P.D. - 《IEEE Transactions on Circuits & Systems I ...
The search method through the function space is defined by: (1) the regression algorithm or technique and (2) the set of parameters related to the search for the learned function f not part of its definition. The targeted variables for the regressors are the concrete properties tackled within...
Similar to the lasso, the adaptive lassois shown to be near-minimax optimal. Furthermore, the adaptive lasso can be solved by the same efficient algorithm for solving the lasso.We also discuss the extension of the adaptive lasso in generalized linear models and show that the oracle properties ...
We also prove the near-minimax optimality of the adaptive lasso shrinkage using the language of Donoho and Johnstone (1994). The adaptive lasso is essentially a con- vex optimization problem with an 1 constraint. Therefore, the adaptive lasso can be solved by the same efficient algorithm ...
A KL-optimum design is obtained from a minimax optimization problem on an infinite-dimensional space. In this paper some important properties of the KL-optimality criterion function are highlighted and an algorithm to construct a KL-optimum design is proposed. It is analytically proved that a ...
This problem is solved by replacing the nondifferentiable maximal eigenvalue of a real nonnegative definite matrix Q with [tr(Q2p)]1/2p. It is shown that any descent algorithm with inexact step-length rule can be used to obtain linear minimax estimators for the parameter vector of a ...
Minimax optimalityReproducibilityStabilityThe stability of statistical analysis is an important indicator for reproducibility, which is one main principle of the scientific method. It entails that similar statistical conclusions can be reached based on independent samples from the same underlying population. ...
The underlying optimization problem involved is a minimax problem which is in general difficult to tract. We address this issue and propose an iterative algorithm named Sequence with LOw Peak sidelobE level (SLOPE) based on the technique of Majorization Minimization, which can be implemented ...