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www.eeworm.com/read/419697/10842968

c alg092.c

/* * SYMMETRIC POWER METHOD ALGORITHM 9.2 * * To approximate the dominant eigenvalue and an associated * eigenvector of the n by n symmetric matrix A given a nonzero vector x: * * INPUT:
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c alg075.c

/* * 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 unknown
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c alg073.c

/* * 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
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c alg083.c

/* * FAST FOURIER TRANSFORM ALGORITHM 8.3 * * To compute the coefficients in the discrete approximation * for the data (x(J),y(J)), 0
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c alg102.c

/* * BROYDEN ALGORITHM 10.2 * * To approximate the solution of the nonlinear system F(X) = 0 * given an initial approximation X. * * INPUT: Number n of equations and unknowns; initial
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c alg094.c

/* * WIELANDT'S DEFLATION ALGORITHM 9.4 * * To approximate the second most dominant eigenvalue and an * associated eigenvector of the n by n matrix A given an * approximation LAMBDA to th
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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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