SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neura...

AIM To investigate the avidity of specific IgG polyribosyl ribitol phosphate (PRP) antibodies induced by three Haemophilus influenzae type b (Hib) conjugate vaccines: PRP-meningococcal outer membrane protein complex (PRP-OMP), PRP-non-toxic mutant diphtheria toxin, CRM197 (HbOC) and PRP-tetanus toxoid (PRP-T). METHODS One hundred and ten infants were immunised with one of the vaccines at two,...

A new active set algorithm (ASA) for large-scale box constrained optimization is introduced. The algorithm consists of a nonmonotone gradient projection step, an unconstrained optimization step, and a set of rules for switching between the two steps. Numerical experiments and comparisons are presented using box constrained problems in the CUTEr and MINPACK test problem libraries. keywords: Nonm...

A hybrid HS and PRP type conjugate gradient method for smooth optimization is presented, which reduces to the classical RPR or HS method if exact linear search is used and converges globally and R-linearly for nonconvex functions with an inexact backtracking line search under standard assumption. An inexact version of the proposed method which admits possible approximate gradient or/and approxi...

In this paper we present a fast iterative image superresolution algorithm using preconditioned conjugate gradient method. To avoid explicitly computing the tolerance in the inverse filter based preconditioner scheme, a new Wiener filter based preconditioner for the conjugate gradient method is proposed to speed up the convergence. The circulant-block structure of the preconditioner allows effic...