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www.eeworm.com/read/386253/8760167
m alg071.m
% 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/386253/8760171
m alg033.m
% HERMITE INTERPOLATION ALGORITHM 3.3
%
% TO OBTAIN THE COEFFICIENTS OF THE HERMITE INTERPOLATING
% POLYNOMIAL H ON THE (N+1) DISTINCT NUMBERS X(0), ..., X(N)
% FOR THE FUNCTION F:
%
% IN
www.eeworm.com/read/386253/8760174
m alg074.m
% ITERATIVE REFINEMENT ALGORITHM 7.4
%
% To approximate the solution to the linear system Ax=b when A is
% suspected to be ill-conditioned:
%
% INPUT: The number of equations and unknowns n; the
www.eeworm.com/read/386253/8760183
m alg055.m
% ADAMS VARIABLE STEP-SIZE PREDICTOR-CORRECTOR ALGORITHM 5.5
%
% To approximate the solution of the initial value problem
% y' = f( t, y ), a
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m alg075.m
% CONJUGATE GRADIENT ALGORITHM 7.5
%
% To solve Ax = b given the preconditioning matrix C inverse
% and an initial approximation
% x(0):
%
% INPUT: the number of equations and unknowns n; the
www.eeworm.com/read/386253/8760189
m alg046.m
% GAUSSIAN TRIPLE INTEGRAL ALGORITHM 4.6
%
% To approximate I = triple integral ( ( f(x,y,z) dz dy dx ) ) with
% limits of integration from a to b for x, from c(x) to d(x) for y, and
% from
www.eeworm.com/read/386253/8760193
m alg062.m
% GAUSSIAN ELIMINATION WITH PARTIAL PIVOTING ALGORITHM 6.2
%
% To solve the n by n linear system
%
% E1: A(1,1) X(1) + A(1,2) X(2) +...+ A(1,n) X(n) = A(1,n+1)
% E2: A(2,1) X(1) + A(2,2) X(2) +
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m alg073.m
% SOR ALGORITHM 7.3
%
% To solve Ax = b given the parameter w and an initial approximation
% x(0):
%
% INPUT: the number of equations and unknowns n; the entries
% A(I,J), 1
www.eeworm.com/read/386253/8760202
m alg104.m
% CONTINUATION METHOD FOR SYSTEMS ALGORITHM 10.4
%
% To approximate the solution of the nonlinear system F(X)=0 given
% an initial approximation X:
%
% INPUT: Number n of equations and unknowns
www.eeworm.com/read/386253/8760203
m alg123.m
% CRANK-NICOLSON ALGORITHM 12.3
%
% To approximate the solution of the parabolic partial-differential
% equation subject to the boundary conditions
% u(0,t) = u(l,t) = 0, 0 < t < T = ma