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...

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...

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...

In this paper, a modified conjugate gradient method is presented for solving large-scale unconstrained optimization problems, which possesses the sufficient descent property with Strong Wolfe-Powell line search. A global convergence result was proved when the (SWP) line search was used under some conditions. Computational results for a set consisting of 138 unconstrained optimization test probl...

2 Method 1 2.1 Optical Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.2 Lucas Kanade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.3 Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.4 Conjugate Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.5 Newton’s Method . . . . . . ...