A dimension-reducing method for unconstrained optimization
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For the unconstrained optimization problem \[ \min f(x),\;x\in\mathbb{R}^n \] a dimension-reducing numerical method is presented. The authors give for this method numerical applications and compare the obtained numerical result with conjugate gradient and variable metric methods.
- University of Patras Greece
Numerical methods based on nonlinear programming, variable metric methods, Imprecise gradient values, Applied Mathematics, dimension-reducing numerical method, comparison of methods, Unconstrained optimization, Quadratic convergence, Dimension-reducing method, Computational Mathematics, Numerical mathematical programming methods, Nonlinear programming, conjugate gradient method, Reduction to one-dimensional equations, unconstrained optimization, Bisection method
Numerical methods based on nonlinear programming, variable metric methods, Imprecise gradient values, Applied Mathematics, dimension-reducing numerical method, comparison of methods, Unconstrained optimization, Quadratic convergence, Dimension-reducing method, Computational Mathematics, Numerical mathematical programming methods, Nonlinear programming, conjugate gradient method, Reduction to one-dimensional equations, unconstrained optimization, Bisection method
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