Gradient methods for large-scale minimization problems

Simulations of Computer, Telecommunications, Control and Social Systems

Gradient methods with Chebyshev relaxation functions are developed. In contrast to the classical gradient procedures, the methods retain the convergence and efficiency for non-convex nonlinear programming problems under the conditions of high stiffness of target functionals and high dimension of the optimized parameters vector.