Rate of convergence

About: Rate of convergence is a research topic. Over the lifetime, 31257 publications have been published within this topic receiving 795334 citations. The topic is also known as: convergence rate.

Binomial Models for Option Valuation - Examining and Improving Convergence

Abstract: Binomial models, which rebuild the continuous setup in the limit, serve for approximative valuation of options, especially where formulas cannot be derived mathematically. Even with the valuation of European call options distorting irregularities occur. For this case, sources of convergence patterns are explained. Furthermore, it is proved order of convergence one for the Cox--Ross--Rubinstein[79] model as well as for the tree parameter selections of Jarrow and Rudd[83], and Tian[93]. Then, we define new binomial models, where the calculated option prices converge smoothly to the Black--Scholes solution and remarkably, we even achieve order of convergence two with much smaller initial error. Notably, solely the formulas to determine the constant up- and down- factors change. Finally, all tree approaches are compared with respect to speed and accuracy calculating relative root--mean--squared error of approximative option values for a sample of randomly selected parameters across a set of refinements. Approximation of American type options with the new models exhibits order of convergence one but smaller initial error than previously existing binomial models.

A Particle-Partition of Unity Method Part V: Boundary Conditions

Abstract: In this sequel to [12, 13, 14, 15] we focus on the implementation of Dirichlet boundary conditions in our partition of unity method. The treatment of essential boundary conditions with meshfree Galerkin methods is not an easy task due to the non-interpolatory character of the shape functions. Here, the use of an almost forgotten method due to Nitsche from the 1970’s allows us to overcome these problems at virtually no extra computational costs. The method is applicable to general point distributions and leads to positive definite linear systems. The results of our numerical experiments, where we consider discretizations with several million degrees of freedom in two and three dimensions, clearly show that we achieve the optimal convergence rates for regular and singular solutions with the (adaptive) h-version and (augmented) p-version.

Generalized Bregman distances and convergence rates for non-convex regularization methods

Abstract: We generalize the notion of Bregman distance using concepts from abstract convexity in order to derive convergence rates for Tikhonov regularization with non-convex regularization terms. In particular, we study the non-convex regularization of linear operator equations on Hilbert spaces, showing that the conditions required for the application of the convergence rates results are strongly related to the standard range conditions from the convex case. Moreover, we consider the setting of sparse regularization, where we show that a rate of order δ1/p holds, if the regularization term has a slightly faster growth at zero than |t|p.

The rate of convergence of the augmented Lagrangian method for nonlinear semidefinite programming

Abstract: We analyze the rate of local convergence of the augmented Lagrangian method in nonlinear semidefinite optimization. The presence of the positive semidefinite cone constraint requires extensive tools such as the singular value decomposition of matrices, an implicit function theorem for semismooth functions, and variational analysis on the projection operator in the symmetric matrix space. Without requiring strict complementarity, we prove that, under the constraint nondegeneracy condition and the strong second order sufficient condition, the rate of convergence is linear and the ratio constant is proportional to 1/c, where c is the penalty parameter that exceeds a threshold $$\overline{c} > 0$$ .