We present the numerical solutions for the PDE-constrained optimization problem arising in cardiac electrophysiology, that is, the optimal control problem of monodomain model. The optimal control problem of monodomain model is a nonlinear optimization problem that is constrained by the monodomain model. The monodomain model consists of a parabolic partial differential equation coupled to a syst...

Recently we suggested a regularization scheme which iteratively adapts regularization parameters by minimizing validation error using simple gradient descent. In this contribution we present an improved algorithm based on the conjugate gradient technique. Numerical experiments with feed-forward neural networks successfully demonstrate improved generalization ability and lower computational cost.

Reinforcement learning by direct policy gradient estimation is attractive in theory but in practice leads to notoriously ill-behaved optimization problems. We improve its robustness and speed of convergence with stochastic meta-descent, a gain vector adaptation method that employs fast Hessian-vector products. In our experiments the resulting algorithms outperform previously employed online sto...

The steepest descent method for large linear systems is well-known to often converge very slowly, with the number of iterations required being about the same as that obtained by utilizing a gradient descent method with the best constant step size and growing proportionally to the condition number. Faster gradient descent methods must occasionally resort to significantly larger step sizes, which...

In recent years, Computer Aided Design (CAD)based on Artificial Neural Networks (ANNs) have been introduced for microwave modeling simulation and optimization. In this paper, the characteristic parameters of Broadside Coupled Coplanar Waveguides (BSCCPWs) have been determined with the use of ANN model. Eight learning algorithms, Levenberg Marquart(LM), Bayesian Regularization (BR),Quasi–Newton ...

The energy dissipation in Wireless Body Area Network (WBAN) systems is the biggest concern as it proportionally affects the system longevity. This energy dissipation in the WBAN system mainly takes place due to the signal interference from other networks causing reduction on the dimensionality. The data prediction in WBAN is also a considerable concern corresponding to misinterpretations and fa...

The standard conjugate gradient (CG) method uses orthogonality of the residues to simplify the formulas for the parameters necessary for convergence. In adaptive filtering, the sample-by-sample update of the correlation matrix and the cross-correlation vector causes a loss of the residue orthogonality in a modified online algorithm, which, in turn, results in loss of convergence and an increase...

A modified spectral PRP conjugate gradient method is presented for solving unconstrained optimization problems. The constructed search direction is proved to be a sufficiently descent direction of the objective function. With an Armijo-type line search to determinate the step length, a new spectral PRP conjugate algorithm is developed. Under some mild conditions, the theory of global convergenc...

A hybrid method combining the FR conjugate gradient method and the WYL conjugate gradient method is proposed for unconstrained optimization problems. The presented method possesses the sufficient descent property under the strong Wolfe-Powell SWP line search rule relaxing the parameter σ < 1. Under the suitable conditions, the global convergence with the SWP line search rule and the weak Wolfe-...