A regularized minimization model with $l_1$-norm penalty (RP) is introduced for training the autoencoders that belong to a class of two-layer neural networks. We show RP can act as an exact which shares same global minimizers, local and d(irectional)-stationary points original under mild conditions. construct bounded box region contains at least one minimizer RP, propose linearly constrained (L...

OBJECTIVE One of the most widely used approaches in electroencephalography/magnetoencephalography (MEG) source imaging is application of an inverse technique (such as dipole modelling or sLORETA) on the component extracted by independent component analysis (ICA) (sensor-space ICA + inverse technique). The advantage of this approach over an inverse technique alone is that it can identify and loc...

We consider a convex optimization problem on linearly constrained cones in a Euclidean Jordan algebra. The cost function consists of a quadratic cost term plus a penalty function. A damped Newton algorithm is proposed for minimization. Quadratic convergence to the global minimum is shown using an explicit step-size selection.

We present a random perturbation of the projected variable metric method for solving linearly constrained nonsmooth (i.e., nondifferentiable) nonconvex optimization problems, and we establish the convergence to a global minimum for a locally Lipschitz continuous objective function which may be nondifferentiable on a countable set of points. Numerical results show the effectiveness of the propos...

We consider a convex optimization problem on linearly constrained cones in Euclidean Jordan algebras. The problem is solved using a damped Newton algorithm. Quadratic convergence to the global minimum is shown using an explicit step-size selection. Moreover, we prove that the algorithm is a smooth discretization of a Newton flow with Lipschitz continuous derivative.

Selecting approaches with appropriate accuracy and suitable speed for the purpose of making decision is one of the managers’ challenges. Also investing decision is one of the main decisions of managers and it can be referred to securities transaction in financial markets which is one of the investments approaches. When some assets and barriers of real world have been considered, optimization of...

This letter presents a linearly constrained constant modulus approach for the blind suppression of multiuser interferences in direct-sequence code division multiple access systems. The method performs the same as minimum mean square error receivers and outperforms existing blind approaches because it only requires a rough estimate of the desired user code and timing.