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c alg028.c

/* * MULLER'S ALGORITHM 2.8 * * To find a solution to f(x) = 0 given three approximations x0, x1 * and x2: * * INPUT: x0,x1,x2; tolerance TOL; maximum number of iterations NO. * * O
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c alg103.c

/* * 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: * *
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c alg125.c

/* * Finite Element Algorithm 12.5 * * To approximate the solution to an elliptic partial-differential * equation subject to Dirichlet, mixed, or Neumann boundary * conditions: * * In
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c alg101.c

/* * NEWTON'S METHOD FOR SYSTEMS ALGORITHM 10.1 * * To approximate the solution of the nonlinear system F(X)=0 given * an initial approximation X: * * INPUT: Number n of equations and
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c alg093.c

/* * INVERSE POWER METHOD ALGORITHM 9.3 * * To approximate an eigenvalue and an associated eigenvector of the * n by n matrix A given a nonzero vector x: * * INPUT: Dimension n; matrix
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c alg065.c

/* * LDL-t ALGORITHM 6.5 * * To factor the positive definite n by n matrix A into LDL**T, * where L is a lower triangular matrix with ones along the diagonal * and D is a diagonal matrix
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m alg072.m

% GAUSS-SEIDEL ITERATIVE TECHNIQUE ALGORITHM 7.2 % % To solve Ax = b given an initial approximation x(0). % % INPUT: the number of equations and unknowns n; the entries % A(I,J), 1
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m alg044.m

% DOUBLE INTEGAL ALGORITHM 4.4 % % To approximate I = double integral ( ( f(x,y) dy dx ) ) with limits % of integration from a to b for x and from c(x) to d(x) for y: % % INPUT: endpoint
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m alg066.m

% CHOLESKI'S ALGORITHM 6.6 % % To factor the positive definite n by n matrix A into LL**T, % where L is lower triangular. % % INPUT: the dimension n; entries A(I,J), 1
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m alg025.m

% METHOD OF FALSE POSITION ALGORITHM 2.5 % % To find a solution to f(x) = 0 given the continuous function % f on the interval [p0,p1], where f(p0) and f(p1) have % opposite signs: % % INP
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