Shifted limited-memory variable metric methods for large-scale unconstrained optimization

AbstractA new family of numerically efficient full-memory variable metric or quasi-Newton methods for unconstrained minimization is given, which give simple possibility to derive related limited-memory methods. Global convergence of the methods can be established for convex sufficiently smooth functions. Numerical experience by comparison with standard methods is encouraging

Automatic differentiation and its program realization

summary:Automatic differentiation is an effective method for evaluating derivatives of function, which is defined by a formula or a program. Program for evaluating of value of function is by automatic differentiation modified to program, which also evaluates values of derivatives. Computed values are exact up to computer precision and their evaluation is very quick. In this article, we describe a program realization of automatic differentiation. This implementation is prepared in the system UFO, but its principles can be applied in other systems. We describe, how the operations are stored in the first part of the derivative computation and how the obtained records are effectively used in the second part of the computation