代码搜索：fprintf
找到约 10,000 项符合「fprintf」的源代码
代码结果 10,000
www.eeworm.com/read/140700/13066050
txt alg103.txt
> restart;
> # 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
www.eeworm.com/read/140700/13066206
txt alg036.txt
> restart;
> # BEZIER CURVE ALGORITHM 3.6
> #
> # To construct the cubic Bezier curves C0, ..., Cn-1 in
> # parameter form, where Ci is represented by
> #
> # (xi(t),yi(t)) = ( a0(i) + a1(i)*t +
www.eeworm.com/read/140700/13066227
txt alg033.txt
> restart;
> # 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 FUNCT
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txt alg054.txt
> restart;
> # ADAMS-FOURTH ORDER PREDICTOR-CORRECTOR ALGORITHM 5.4
> #
> # To approximate the solution of the initial value problem
> # y' = f(t,y), a
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txt alg101.txt
> restart;
> # 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 eq
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txt alg052.txt
> restart;
> # RUNGE-KUTTA (ORDER 4) ALGORITHM 5.2
> #
> # TO APPROXIMATE THE SOLUTION TO THE INITIAL VALUE PROBLEM:
> # Y' = F(T,Y), A
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txt alg031.txt
> restart;
> # NEVILLE'S ITERATED INTERPOLATION ALGORITHM 3.1
> #
> # To evaluate the interpolating polynomial P on the
> # (n+1) distinct numbers x(0), ..., x(n) at the number x
> # for the func
www.eeworm.com/read/140700/13066302
txt alg057.txt
> restart;
> # RUNGE-KUTTA FOR SYSTEMS OF DIFFERENTIAL EQUATIONS ALGORITHM 5.7
> #
> # TO APPROXIMATE THE SOLUTION OF THE MTH-ORDER SYSTEM OF FIRST-
> # ORDER INITIAL-VALUE PROBLEMS
> #
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txt alg063.txt
> restart;
> # GAUSSIAN ELIMINATION WITH SCALED PARTIAL PIVOTING ALGORITHM 6.3
> #
> # 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]
> # E
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txt alg103.txt
> restart;
> # 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