In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression. Using a quasi-newton framework we extend this to group-penalized multinomial regression. We give the first publicly available implementation for these in R, and compare the speed of this algorithm to a similar algorithm for standard `1-penalized multinomial regression on simulated data — we show...

We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e.g., the size may exceed computer memory or data are collected in real-time). In our proposed framework, stochasticity is introduced in two different frameworks as a means to overcome these compu...

Applications in engineering frequently require the adjustment of certain parameters. While the mathematical laws that determine these parameters often are well understood, due to time limitations in every day industrial life, it is typically not feasible to derive an explicit computational procedure for adjusting the parameters based on some given measurement data. This paper aims at showing th...

An efficient multi-block Newton–Krylov algorithm using the compressible Navier–Stokes equations is presented for the analysis and design of high-lift airfoil configurations. The preconditioned generalized minimum residual (GMRES) method is applied to solve the discreteadjoint equation, leading to a fast computation of accurate objective function gradients. Furthermore, the GMRES method is used ...

Aiming at the problems of low reconstruction rate and poor precision when reconstructing sparse signals in wireless sensor networks, a signal algorithm based on Limit-Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is proposed. The L-BFGS uses two-loop recursion to find descent direction dk directly by calculating step difference between m adjacent iteration points, matrix Hk appr...

{ We describe a Newton method applied to the evaluation of a critical point of a total energy associated to a shape optimization problem. The key point of these methods is the Hessian of the shape functional. We give an expression of the Hessian as well as the relation with the second-order Eulerian semi-derivative. An application to the electromagnetic shaping of liquid metals process is studi...

Many algorithms have been proposed for finding a local minimum of a function f ( x ) f rom R" into R ~ using only function and gradient values. These include discrete Newton methods, quasi -Newton methods, and the conjugate gradient algorithm. For a discussion of these methods, see, for example [1]. There are classes of problems for which each of these methods is the most efficient. In this pap...