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is a better approximation of the root than x 0. Geometrically, (x 1, 0) is the x-intercept of the tangent of the graph of f at (x 0, f(x 0)): that is, the improved guess, x 1, is the unique root of the linear approximation of f at the initial guess, x 0. The process is repeated as
The geometric interpretation of Newton's method is that at each iteration, it amounts to the fitting of a parabola to the graph of () at the trial value , having the same slope and curvature as the graph at that point, and then proceeding to the maximum or minimum of that parabola (in higher dimensions, this may also be a saddle point), see below.
The Taylor series of f converges uniformly to the zero function T f (x) = 0, which is analytic with all coefficients equal to zero. The function f is unequal to this Taylor series, and hence non-analytic. For any order k ∈ N and radius r > 0 there exists M k,r > 0 satisfying the remainder bound above.
Consider a quadratic form given by f(x,y) = ax 2 + bxy + cy 2 and suppose that its discriminant is fixed, say equal to −1/4. In other words, b 2 − 4ac = 1. One can ask for the minimal value achieved by | (,) | when it is evaluated at non-zero vectors of the grid , and if this minimum does not exist, for the infimum.
Quasi-Newton methods for optimization are based on Newton's method to find the stationary points of a function, points where the gradient is 0. Newton's method assumes that the function can be locally approximated as a quadratic in the region around the optimum, and uses the first and second derivatives to find the stationary point.
This is the quadratic function whose first and second derivatives are the same as those of f at a given point. The formula for the best quadratic approximation to a function f around the point x = a is () + ′ () + ″ (). This quadratic approximation is the second-order Taylor polynomial for the function centered at x = a.
We then use this new value of x as x 2 and repeat the process, using x 1 and x 2 instead of x 0 and x 1. We continue this process, solving for x 3 , x 4 , etc., until we reach a sufficiently high level of precision (a sufficiently small difference between x n and x n −1 ):
First suppose that the surface is the graph of a twice continuously differentiable function, z = f(x,y), and that the plane z = 0 is tangent to the surface at the origin. Then f and its partial derivatives with respect to x and y vanish at (0,0). Therefore, the Taylor expansion of f at (0,0) starts with quadratic terms: