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The Barzilai-Borwein method [1] is an iterative gradient descent method for unconstrained optimization using either of two step sizes derived from the linear trend of the most recent two iterates. This method, and modifications, are globally convergent under mild conditions, [ 2 ] [ 3 ] and perform competitively with conjugate gradient methods ...
The version of Steffensen's method implemented in the MATLAB code shown below can be found using the Aitken's delta-squared process for accelerating convergence of a sequence. To compare the following formulae to the formulae in the section above, notice that x n = p − p n . {\displaystyle x_{n}=p\,-\,p_{n}~.}
The conjugate residual method is an iterative numeric method used for solving systems of linear equations.It's a Krylov subspace method very similar to the much more popular conjugate gradient method, with similar construction and convergence properties.
For example, a hyperboloid of one sheet is a quadric surface in ruled by two different families of lines, one line of each passing through each point of the surface; each family corresponds under the Plücker map to a conic section within the Klein quadric in .
The Matlab function ode45 implements a one-step method that uses two embedded explicit Runge-Kutta methods with convergence orders 4 and 5 for step size control. [ 29 ] The solution can now be plotted, y 1 {\displaystyle y_{1}} as a blue curve and y 2 {\displaystyle y_{2}} as a red curve; the calculated points are marked by small circles:
In numerical analysis, the ITP method (Interpolate Truncate and Project method) is the first root-finding algorithm that achieves the superlinear convergence of the secant method [1] while retaining the optimal [2] worst-case performance of the bisection method. [3]
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).
[4] [5] There are now extensions that consider cases when there are more than two sets, or when the sets are not convex, [6] or that give faster convergence rates. Analysis of POCS and related methods attempt to show that the algorithm converges (and if so, find the rate of convergence), and whether it converges to the projection of the ...