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Log-linear plots of the example sequences a k, b k, c k, and d k that exemplify linear, linear, superlinear (quadratic), and sublinear rates of convergence, respectively. Convergence rates to fixed points of recurrent sequences
The following iterates are 1.0103, 1.00093, 1.0000082, and 1.00000000065, illustrating quadratic convergence. This highlights that quadratic convergence of a Newton iteration does not mean that only few iterates are required; this only applies once the sequence of iterates is sufficiently close to the root. [16]
A comparison of the convergence of gradient descent with optimal step size (in green) and conjugate vector (in red) for minimizing a quadratic function associated with a given linear system. Conjugate gradient, assuming exact arithmetic, converges in at most n steps, where n is the size of the matrix of the system (here n = 2).
If an equation can be put into the form f(x) = x, and a solution x is an attractive fixed point of the function f, then one may begin with a point x 1 in the basin of attraction of x, and let x n+1 = f(x n) for n ≥ 1, and the sequence {x n} n ≥ 1 will converge to the solution x.
This means that the false position method always converges; however, only with a linear order of convergence. Bracketing with a super-linear order of convergence as the secant method can be attained with improvements to the false position method (see Regula falsi § Improvements in regula falsi) such as the ITP method or the Illinois method.
Linear-quadratic regulator — system dynamics is a linear differential equation, objective is quadratic; Linear-quadratic-Gaussian control (LQG) — system dynamics is a linear SDE with additive noise, objective is quadratic Optimal projection equations — method for reducing dimension of LQG control problem
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.
Therefore, the method has linear convergence with rate /. Golden-section search : This is a variant in which the points b , c are selected based on the golden ratio . Again, only one function evaluation is needed in each iteration, and the method has linear convergence with rate 1 / φ ≈ 0.618 {\displaystyle 1/\varphi \approx 0.618} .