Finite difference operators from moving least squares interpolation

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Stable Moving Least-Squares

It is a common procedure for scattered data approximation to use local polynomial fitting in the least-squares sense. An important instance is the Moving Least-Squares where the corresponding weights of the data site vary smoothly, resulting in a smooth approximation. In this paper we build upon the techniques presented by Wendland and present a somewhat simpler error analysis of the MLS approx...

متن کامل

Moving Least Squares Coordinates

We propose a new family of barycentric coordinates that have closed-forms for arbitrary 2D polygons. These coordinates are easy to compute and have linear precision even for open polygons. Not only do these coordinates have linear precision, but we can create coordinates that reproduce polynomials of a set degree m as long as degree m polynomials are specified along the boundary of the polygon....

متن کامل

Moving Least Squares Approximation

An alternative to radial basis function interpolation and approximation is the so-called moving least squares method. As we will see below, in this method the approximation Pf to f is obtained by solving many (small) linear systems, instead of via solution of a single – but large – linear system as we did in the previous chapters. To make a connection with the previous chapters we start with th...

متن کامل

Least-squares lattice interpolation filters

This paper develops a time as well as order update recursion for linear least-squares lattice (LSL) interpolation filters. The LSL interpolation filter has the nice stage-to-stage modularity which allows its length to be increased or decreased "two-sidedly" (i.e., both pust and future) without affecting the already computed parameters. The LSL interpolation filter is also efficient in computati...

متن کامل

Least-Squares Temporal Difference Learning

Excerpted from: Boyan, Justin. Learning Evaluation Functions for Global Optimization. Ph.D. thesis, Carnegie Mellon University, August 1998. (Available as Technical Report CMU-CS-98-152.) TD( ) is a popular family of algorithms for approximate policy evaluation in large MDPs. TD( ) works by incrementally updating the value function after each observed transition. It has two major drawbacks: it ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: ESAIM: Mathematical Modelling and Numerical Analysis

سال: 2007

ISSN: 0764-583X,1290-3841

DOI: 10.1051/m2an:2007042