Some global convergence properties of a variable metric algorithm for minimization without exact line searches, in R. 23 superlinear convergent algorithm for minimizing the Moreau-Yosida regularization F. However, this algorithm makes use of the generalized Jacobian of F, instead of matrices B k generated by a quasi-Newton formula. Moreover, the line search is performed on the function F , rath...

A gradient-based Newton–Krylov algorithm is presented for the aerodynamic shape optimization of singleand multi-element airfoil configurations. The flow is governed by the compressible Navier–Stokes equations in conjunction with a one-equation transport turbulence model. The preconditioned generalized minimal residual method is applied to solve the discrete-adjoint equation, which leads to a fa...

In this paper, we consider the problem of efficiently computing the eigenvalues of limited-memory quasi-Newton matrices that exhibit a compact formulation. In addition, we produce a compact formula for quasi-Newton matrices generated by any member of the Broyden convex class of updates. Our proposed method makes use of efficient updates to the QR factorization that substantially reduces the cos...

We investigate reduced-space Newton-Krylov (NK) algorithms for engineering parameter optimization problems constrained by partial-differential equations. We review reduced-space and full-space optimization algorithms, and we show that the efficiency of the reduced-space strategy can be improved significantly with inexact-Hessianvector products computed using approximate second-order adjoints. R...

This paper studies the neural network nonlinear autoregressive with exogenous signal (NNARX) model identification of elastic and inelastic hydro power plant. A nonlinear relationship between the turbine deviated power and random gate position on random load variation and water disturbance is assessed. The identified elastic NNARX hydro plant model is simulated with predictive controller to trac...

A new approach to the problem of minimizing L2sensitivity subject to L2-norm scaling constraints for state-space digital filters is proposed. Using linearalgebraic techniques, the problem at hand is converted into an unconstrained optimization problem, and the unconstrained problem obtained is then solved by applying an efficient quasi-Newton algorithm. Computer simulation results are presented...

Since 1965, there has been significant progress in the theoretical study on quasi-Newton methods for solving nonlinear equations, especially in the local convergence analysis. However, the study on global convergence of quasi-Newton methods is relatively fewer, especially for the BFGS method. To ensure global convergence, some merit function such as the squared norm merit function is typically ...

Multiframe super-resolution algorithms can be used to reconstruct a high-quality high-resolution image from several warped, blurred, undersampled, and possibly noisy images. A widely used means of implementing such algorithms is by optimization-based model inversion. In the past, steepest-descent methods have been applied. While easy to implement, these methods are known for their poor converge...

Optimal control problems for finite-strain elasticity are considered. An inner pressure or an inner fiber tension is acting as a driving force. Such internal forces are typical, for instance, for the motion of heliotropic plants, and for muscle tissue. Non-standard objective functions relevant for elasticity problems are introduced. Optimality conditions are derived on a formal basis, and a lim...

This note focuses on developing quasi-Newton methods that combine m+ 1 multistep and single-step updates on a single iteration for the sake of constructing the new approximation to the Hessian matrix to be used on the next iteration in computing the search direction. The approach considered here exploits the merits of the multistepmethods and those of El-Baali (1999) to create a hybrid techniqu...