Newton's method in optimization
In calculus, Newton's method is an iterative method for finding the roots of a differentiable function F, which are solutions to the equation F (x) = 0. As such, Newton's method can be applied to the derivative f ′ of a twice-differentiable function f to find the roots of the derivative (solutions to f ′(x) = 0), also known as the critical points of f. These solutions may be minima, maxima, or saddle points; see section "Several variables" in Critical point (mathematics) and also section in this article. This is relevant in optimization, which aims to find (global) minima of the function f.
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- enIn calculus, Newton's method is an iterative method for finding the roots of a differentiable function F, which are solutions to the equation F (x) = 0. As such, Newton's method can be applied to the derivative f ′ of a twice-differentiable function f to find the roots of the derivative (solutions to f ′(x) = 0), also known as the critical points of f. These solutions may be minima, maxima, or saddle points; see section "Several variables" in Critical point (mathematics) and also section in this article. This is relevant in optimization, which aims to find (global) minima of the function f.
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- enIn calculus, Newton's method is an iterative method for finding the roots of a differentiable function F, which are solutions to the equation F (x) = 0. As such, Newton's method can be applied to the derivative f ′ of a twice-differentiable function f to find the roots of the derivative (solutions to f ′(x) = 0), also known as the critical points of f. These solutions may be minima, maxima, or saddle points; see section "Several variables" in Critical point (mathematics) and also section in this article. This is relevant in optimization, which aims to find (global) minima of the function f.
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- Calculus
- Category:Optimization algorithms and methods
- Cholesky factorization
- Conjugate gradient method
- Conjugate residual method
- Constrained optimization
- Critical point (mathematics)
- Derivative
- Differentiable function
- Equation
- File:Newton optimization vs grad descent.svg
- Gauss–Newton algorithm
- Gradient
- Gradient descent
- Graph of a function
- Hessian matrix
- Invertible matrix
- Iteration
- Iterative method
- Iterative methods
- John Wiley & Sons
- Lagrange multipliers
- Learning rate
- Levenberg–Marquardt algorithm
- Mathematical optimization
- Mike Bostock
- Multiplicative inverse
- Nelder–Mead method
- Newton's method
- Optimization (mathematics)
- Parabola
- Quasi-Newton method
- Saddle point
- Sequence
- Smooth function
- System of linear equations
- Taylor expansion
- Trust region
- Wolfe conditions
- Zero of a function
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- Metoda Newtona (optymalizacja)
- Newton's method in optimization
- Newtons metode i optimering
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- Category:Optimization algorithms and methods
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