Numerical mathematics: theory and computer applications
Numerical mathematics: theory and computer applications
The generalized inverse matrix and the surface-surface intersection problem
Theory and practice of geometric modeling
The Newton Bracketing Method for Convex Minimization
Computational Optimization and Applications
A heuristic method for large-scale multi-facility location problems
Computers and Operations Research
Directional secant method for nonlinear equations
Journal of Computational and Applied Mathematics
The Newton Bracketing method for the minimization of convex functions subject to affine constraints
Discrete Applied Mathematics
Directional secant method for nonlinear equations
Journal of Computational and Applied Mathematics
A convergence analysis for directional two-step Newton methods
Numerical Algorithms
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Directional Newton methods for functions f of n variables are shown to converge, under standard assumptions, to a solution of f(x) = 0. The rate of convergence is quadratic, for near-gradient directions, and directions along components of the gradient of f with maximal modulus. These methods are applied to solving systems of equations without reversion of the Jacobian matrix.