代码搜索：Approximation
找到约 1,542 项符合「Approximation」的源代码
代码结果 1,542
www.eeworm.com/read/386253/8760129
m alg103.m
% STEEPEST DESCENT ALGORITHM 10.3
%
% To approximate a solution P to the minimization problem
% G(P) = MIN( G(X) : X in R(n) )
% given an initial approximation X:
%
% INPUT: Num
www.eeworm.com/read/366144/9828328
tcl gmmake_circ.tcl
proc gmmake_circ numsubdiv {
# gmmake_circ $numsubdiv
# Make a cubic bezier approximation to a circle. Argument
# is the number of subdivisions.
if {$numsubdiv < 3} {
error "Number
www.eeworm.com/read/419697/10842884
c alg071.c
/*
* JACOBI ITERATIVE ALGORITHM 7.1
*
* To solve Ax = b given an initial approximation x(0).
*
* INPUT: the number of equations and unknowns n; the entries
* A(I,J), 1
www.eeworm.com/read/296477/8101269
m rbf.m
%% Radial Basis Approximation
% This demo uses the NEWRB function to create a radial basis network that
% approximates a function defined by a set of data points.
%
% Copyright 1992-2002 The MathW
www.eeworm.com/read/245849/12777830
m cumquad.m
function ci = cumquad(y,x)
% Function computes the numerical approximation to the indefinite
% integral y dx (corresponding to cumsum)
% y ordinates
% x abscissas
% If only one input argume
www.eeworm.com/read/244727/12847671
m colinge.m
function [uh, p, t] = colinge;
% COLINGE FE implementation of Colinge and Rappaz (1999)
% [uh, p, t] = colinge computes a finite element solution for a first order
% approximation of the flow of
www.eeworm.com/read/140698/13066487
c alg071.c
/*
* JACOBI ITERATIVE ALGORITHM 7.1
*
* To solve Ax = b given an initial approximation x(0).
*
* INPUT: the number of equations and unknowns n; the entries
* A(I,J), 1
www.eeworm.com/read/140697/13066829
m alg103.m
% STEEPEST DESCENT ALGORITHM 10.3
%
% To approximate a solution P to the minimization problem
% G(P) = MIN( G(X) : X in R(n) )
% given an initial approximation X:
%
% INPUT: Num
www.eeworm.com/read/140697/13067023
m alg103.m
% STEEPEST DESCENT ALGORITHM 10.3
%
% To approximate a solution P to the minimization problem
% G(P) = MIN( G(X) : X in R(n) )
% given an initial approximation X:
%
% INPUT: Num
www.eeworm.com/read/238825/13322314
m contents.m
% Signal processing functions
%
% simplex : Simplex routine (see fmins matlab function)
% derivative : Derivative approximation
% leasqr : Non Linear Least Square multivariable fit.
% leasqrexamp : le