Crăciun Iancu
Tiberiu Popoviciu Institute of Numerical Analysis Romania Academy, Cluj-Napoca, Romania
Tiberiu Oproiu
Romanian Academy Astronomical Observatory
Ion Păvăloiu Tiberiu Popoviciu Institute of Numerical Analysis Romania Academy, Cluj-Napoca, Romania
Keywords
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Paper coordinates
C. Iancu, T. Oproiu, I. Păvăloiu, Inverse interpolating splines with applications to the equation solving, Seminar of Functional Analysis and Numerical Method, Babes-Bolyai University, Faculty of Mathematics, Preprint nr. 1, 1986, pp. 67-82.
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[5] Iancu, C., On the Cubic Spline of Interpolation, Seminar of Functional Analysis and Numerical Methods, Babsc-Bolyai University, Preprint 4 (1981), 52-71
[6] Iancu, C., P[v[loiu, I., La resolution des equations par interpolation inverse de type Hermite, Seminar of Functional analysis and Numerical Methods, Babs-Bolyai University, Preprint 4 (1981), 72-84
[7] Iancu, C., Păvăloiu, I., Resolution des equations a l’aide des fonctions epline d’interpolation inverse, Seminar Functional Analysis and Numerical Methods, Babeș-Bolyai University, Preprint 1 (1984), 97-104
[8] Imamov, A., Reshenie nelinejnykh uravnenii metodom obratnogo splajn interpolirovanie, Metody splajnfunktsii, Akad. Nauk. SSSR, Novosibirsk, 81 (1979), 74-80
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INVERSE INTERPOLATING SPLINES WITH APPLICATIONS TO THE EQUATION SOLVING
C. IANCU, T. OPROIU and I. PĀVĀLOIU
INTRODUCTION
Let ff be the function:
(1)
f:[a,b]rarrR,f:[a, b] \rightarrow \mathbb{R},
and:
{:(2)Delta_(x):a=x_(0) < x_(1) < dots < x_(n-1) < x_(n)=b","quad n >= 2","n inN",":}\begin{equation*}
\Delta_{x}: a=x_{0}<x_{1}<\ldots<x_{n-1}<x_{n}=b, \quad n \geqslant 2, n \in \mathbb{N}, \tag{2}
\end{equation*}
a partition of the interval [a,b][\mathrm{a}, \mathrm{b}].
We assume that the function ff is twice derivable on the interval [a,b][\mathrm{a}, \mathrm{b}], and f^(')(x)!=0\mathrm{f}^{\prime}(\mathrm{x}) \neq 0 for all xin[a,b]\mathrm{x} \in[\mathrm{a}, \mathrm{b}]. We denote F=f([a,b])F=f([a, b]).
From the above assumption it results that the function ff is a bijection; hence there exists f^(-1):F rarr[a,b]f^{-1}: F \rightarrow[a, b].
Since we have assumed that the function ff is twice
derivable on the interval [a,b][a, b], and f^(')(x)!=0,AA x in[a,b]f^{\prime}(x) \neq 0, \forall x \in[a, b], it results that the function f^(-1)f^{-1} will also be twice derivable on the set FF, and:
Next, we deal with the elaboration of a numerical method for solving certain equations of the form:
{:(4)f(x)=0","quad x in[a","b]:}\begin{equation*}
f(x)=0, \quad x \in[a, b] \tag{4}
\end{equation*}
using the inverse interpolating cubic splines.
Assuming that the equation (4) admits a root bar(x)in[a,b]\bar{x} \in[a, b], then from f( bar(x))=0f(\bar{x})=0 it follows that f^(-1)(0)= bar(x)f^{-1}(0)=\bar{x}, while from f^(')(x)!=0f^{\prime}(x) \neq 0 for all x in[a,b]x \in[a, b] it follows the uniqueness of bar(x)\bar{x}.
We shall remark the following two cases for solving the equation (4):
A. The values f_(i)=f(x_(i)),i= bar(0,n)f_{i}=f\left(x_{i}\right), i=\overline{0, n}, of the function ff on the knots of the partition (2) are given, and f^(')(x_(0))==m_(o),f^('')(x_(o))=M_(o)f^{\prime}\left(x_{0}\right)= =m_{o}, f^{\prime \prime}\left(x_{o}\right)=M_{o}, where m_(o),M_(o)inR,m_(o)!=0m_{o}, M_{o} \in \mathbb{R}, m_{o} \neq 0, are also given.
B. The values of the function ff on every point of the interval [a,b][\mathrm{a}, \mathrm{b}] are known.
In order to solve the equation (4) in each case (A or BB ), we shall construct an inverse interpolating spline.
[ d _("d ")_{\text {d }} = 7: 2. INVERSE IMTERPOLATIMG SPLIMES
Let [alpha,beta]subeF[\alpha, \beta] \subseteq \mathcal{F} be the shortest interval containing the
values f_(i)=f(x_(i)),i= bar(0,n)f_{i}=f\left(x_{i}\right), i=\overline{0, n}, of the function ff on the knots of the partition (2).
Since ff is bijective on [a,b][a, b], it is either increasing or decreasing on this interval.
We shall consider further that ff is increasing, and we shall denote by:
for the calculation of the values D_(i)^(')D_{i}^{\prime} and D_(i)^(''),i= bar(0,n-1)D_{i}^{\prime \prime}, i=\overline{0, n-1}.
Knowing the restrictions bar(H)_(i)\bar{H}_{i} for i= bar(0,n-1)i=\overline{0, n-1}, if we have determined an interval ( x_(j),x_(j+1)x_{j}, x_{j+1} ) in which the root bar(x)\bar{x} of the given equation (4) is lying, since bar(H)_(j)\bar{H}_{j} approaches the function f^(-1)f^{-1} for x in(x_(j),x_(j+1))x \in\left(x_{j}, x_{j+1}\right), it results the following approximation for the root bar(x)\bar{x} :
3. ANOTHER FORM FOR THE INVERSE INTERPOLATIEG SPLINE
In this section we shall give a new form for the inverae interpolating spline (6). For this purpose, using the model proposed in [5], we shall construct the cubic interpolating apline for f^(-1)f^{-1} on the knots of the partition (5), whom restriction on the interval [f_(i-1),f_(i)]\left[f_{i-1}, f_{i}\right] has the following form:
Denoting by H(3,Delta_(f))H\left(3, \Delta_{f}\right) the set of all the cubic interpolating splines corresponding to the partition (5), the following proposition holds:
Proposition 1. Every spline bar(H)in H(3,Delta_(f))\bar{H} \in H\left(3, \Delta_{f}\right) which has the form (16) is uniquely determined if it satisfies the following conditions:
Since, in the problem we have studied, we have 'D' = =a_(1)=1//f^(')(x_(o))=a_{1}=1 / f^{\prime}\left(x_{o}\right) and D_(o)^('')=a_(2)=-f^('')(x_(o))//(f^(')(x_(o)))^(3)D_{o}^{\prime \prime}=a_{2}=-f^{\prime \prime}\left(x_{o}\right) /\left(f^{\prime}\left(x_{o}\right)\right)^{3} are known, it follows that the system (18) is compatible determined and provides the values D_(1)^('),D_(2)^('),dots,D_(n)^('),D_(1)^(''),D_(2)^(''),dots,D_(n)^('')D_{1}^{\prime}, D_{2}^{\prime}, \ldots, D_{n}^{\prime}, D_{1}^{\prime \prime}, D_{2}^{\prime \prime}, \ldots, D_{n}^{\prime \prime} which determine the cubic spline whose restrictions are of the form (16).
Note. If the function ff is decreasing, one proceeds analogously, performing a rearrangement of the knots from right to left and taking the values D_(n)^(')D_{n}^{\prime} and D_(n)^('')D_{n}^{\prime \prime} instead of D_(o)^(')D_{o}^{\prime} and D_(o)^('')D_{o}^{\prime \prime}, respectively.
4. SOLUTION OF THE EQUATION f(x)=0f(x)=0
The sign of the values f_(1),i= bar(0,n)f_{1}, i=\overline{0, n}, of the partition (5) points out the interval (x_(j),x_(j+1))sub(a,b)\left(x_{j}, x_{j+1}\right) \subset(a, b) in which the root bar(x)\bar{x} of the equation (4) is lying.
The method we present in this paper is given by the formulae (15) and (16), obtaining the following approximation for the root bar(x)\bar{x} :
(19) quad bar(x)~= bar(H)_(j)(0)=((D_(j+1)^('')-D_(j)^(''))//(6k_(j+1)))(-f_(j))^(3)+\quad \bar{x} \cong \bar{H}_{j}(0)=\left(\left(D_{j+1}^{\prime \prime}-D_{j}^{\prime \prime}\right) /\left(6 k_{j+1}\right)\right)\left(-f_{j}\right)^{3}+ +(D_(j)^('')//2)(-f_(j))^(2)+D_(j)^(')(-f_(j))+x_(j)+\left(D_{j}^{\prime \prime} / 2\right)\left(-f_{j}\right)^{2}+D_{j}^{\prime}\left(-f_{j}\right)+x_{j}.
5. ESTTILATE OF THE ERROR
Theorem 1. Let the function (1) be increasing. If the inverse function f^(-1)f^{-1} is approximated on the interval [f_(i-1):}\left[f_{i-1}\right., {:f_(i)],i= bar(I,n)\left.f_{i}\right], i=\overline{I, n}, by a cubic apline of the form (16), observing the conditions (17) and D_(1)^(%)-D_(1-1)^(#) > 0D_{1}^{\%}-D_{1-1}^{\#}>0, then the following inequalities hold:
(20) quad| bar(H)_(i-1)(y)-f^(-1)(y)| < {[delta_(x)","," for "D_(i-1)^('') > 0","D_(i-1)^(') > 0;],[delta_(x)-2D_(i-1)^(')k_(i)","," for "D_(i-1)^('') > 0","],[delta_(x)-D_(i-1)^('')k_(i)^(2)","," for "D_(i-1)^('') < 0;],[D_(i-1) > 0;,],[delta_(x)-2D_(i-1)k_(i)-D_(i-1)^('')k_(i)^(2)","" for "],[D_(i-1)^('') < 0","D_(i-1) < 0","]:}\quad\left|\bar{H}_{i-1}(y)-f^{-1}(y)\right|<\left\{\begin{array}{rr}\delta_{x}, & \text { for } D_{i-1}^{\prime \prime}>0, D_{i-1}^{\prime}>0 ; \\ \delta_{x}-2 D_{i-1}^{\prime} k_{i}, & \text { for } D_{i-1}^{\prime \prime}>0, \\ \delta_{x}-D_{i-1}^{\prime \prime} k_{i}^{2}, & \text { for } D_{i-1}^{\prime \prime}<0 ; \\ D_{i-1}>0 ; & \\ \delta_{x}-2 D_{i-1} k_{i}-D_{i-1}^{\prime \prime} k_{i}^{2}, \text { for } \\ D_{i-1}^{\prime \prime}<0, D_{i-1}<0,\end{array}\right.
where delta_(x)=x_(i)-2x_(i-1)+x,quad x in[x_(i-1),x_(i)],quad i= bar(1,n)\delta_{x}=x_{i}-2 x_{i-1}+x, \quad x \in\left[x_{i-1}, x_{i}\right], \quad i=\overline{1, n}.
and then:
(21) | bar(H)_(i-1)(y)-f^(-1)(y)| < |(D_(i)^('')-D_(i-1)^(''))//(6k_(i))||y-f_(i-1)|^(3)+\left|\bar{H}_{i-1}(y)-f^{-1}(y)\right|<\left|\left(D_{i}^{\prime \prime}-D_{i-1}^{\prime \prime}\right) /\left(6 k_{i}\right)\right|\left|y-f_{i-1}\right|^{3}+ +|D_(i-1)^(n)//2||(y^(˙))-f_(i-1)|^(2)+|D_(i-1)||y-f_(i-1)|+|x_(i-1)-x|+\left|D_{i-1}^{n} / 2\right|\left|\dot{y}-f_{i-1}\right|^{2}+\left|D_{i-1}\right|\left|y-f_{i-1}\right|+\left|x_{i-1}-x\right|.
From the conditions of the theorem we have |y-p_(i-1)| <\left|y-p_{i-1}\right|< (:|f_(i)-f_(i-1)∣=k_(i)\langle | f_{i}-f_{i-1} \mid=k_{i}, and then the inequality (21) can be put in the form:
(22) | bar(H)_(i-1)(y)-f^(-1)(y)| < ((D_(i)^(n)-D_(i-1)^(n))//(6k_(i)))k_(i)^(3)+|D_(i-1)^(n)//2|k_(i)^(2)+\left|\bar{H}_{i-1}(y)-f^{-1}(y)\right|<\left(\left(D_{i}^{n}-D_{i-1}^{n}\right) /\left(6 k_{i}\right)\right) k_{i}^{3}+\left|D_{i-1}^{n} / 2\right| k_{i}^{2}+
For D_(i-1) > 0D_{i-1}>0, we obtain from the inequality (24):
(25) | bar(H)_(i-1)(y)-f^(-1)(y)| < x_(i)-2x_(i-1)+x=delta_(x)\left|\bar{H}_{i-1}(y)-f^{-1}(y)\right|<x_{i}-2 x_{i-1}+x=\delta_{x},
while for D_(i-1) < 0D_{i-1}<0 we have:
(26) | bar(H)_(i-1)(y)-p^(-1)(y)| < delta_(x)-2D_(i-1)k_(i)\left|\bar{H}_{i-1}(y)-p^{-1}(y)\right|<\delta_{x}-2 D_{i-1} k_{i}.
If D_(i-1)^(n) < 0D_{i-1}^{n}<0, then we obtain from (22):
(27) | bar(H)_(i-1)(y)-f^(-1)(y)| < (D_(i)^(n)//6)k_(i)^(2)-(D_(i-1)^(n)//6)k_(i)^(2)-(D_(i-1)^(n)//2)k_(i)^(2)+\left|\bar{H}_{i-1}(y)-f^{-1}(y)\right|<\left(D_{i}^{n} / 6\right) k_{i}^{2}-\left(D_{i-1}^{n} / 6\right) k_{i}^{2}-\left(D_{i-1}^{n} / 2\right) k_{i}^{2}+
Taking into account the first equality (18), the inequality (27) will acquire the following form:
(28) | bar(H)_(i-1)(y)-p^(-1)(y)| < (k_(i)^(2)//6)(6(x_(i)-x_(i-1))//k_(i)^(2)-6D_(i-1)//k_(i)-:}\left|\bar{H}_{i-1}(y)-p^{-1}(y)\right|<\left(k_{i}^{2} / 6\right)\left(6\left(x_{i}-x_{i-1}\right) / k_{i}^{2}-6 D_{i-1} / k_{i}-\right.
For D_(i-1) > 0D_{i-1}>0, the inequality (28) provides the following result:
(29) | bar(H)_(i-1)(y)-p^(-1)(y)| < delta_(x)-D_(i-1)^(n)k_(i)^(2)\left|\bar{H}_{i-1}(y)-p^{-1}(y)\right|<\delta_{x}-D_{i-1}^{n} k_{i}^{2},
while for D_(-1) < 0D_{-1}<0 we obtain from the same inequality (28) the result:
(30) | bar(H)_(i-1)(y)-f^(-1)(y)| < delta_(x)-2D_(i-1)k_(i)-D_(i-1)^(n)k_(i)^(2)\left|\bar{H}_{i-1}(y)-f^{-1}(y)\right|<\delta_{x}-2 D_{i-1} k_{i}-D_{i-1}^{n} k_{i}^{2}.
So, theorem 1 is proved.
Theorem 2. In the conditions of theorem 1, if the function ff is decreasing, then the following inequalities hold:
(31) | bar(H)_(i-1)(y)-f^(-1)(y)| < {[alpha_(x)","" for "D_(i)^('') < 0","D_(i)^(') < 0;],[alpha_(x)-2D_(i)^(')*k_(i)","" for "D_(i)^('') < 0","D_(i)^(') > 0;],[alpha_(x)+D_(i)^('')*k_(i)^(2)","" for "D_(i)^('') > 0","D_(i)^(') < 0;],[alpha_(x)+D_(i)^('')*k_(i)^(2)-2D_(i)*k_(i)","" for "D_(i)^('') > 0","],[D_(i) > 0","]:}\left|\bar{H}_{i-1}(y)-f^{-1}(y)\right|<\left\{\begin{array}{l}\alpha_{x}, \text { for } D_{i}^{\prime \prime}<0, D_{i}^{\prime}<0 ; \\ \alpha_{x}-2 D_{i}^{\prime} \cdot k_{i}, \text { for } D_{i}^{\prime \prime}<0, D_{i}^{\prime}>0 ; \\ \alpha_{x}+D_{i}^{\prime \prime} \cdot k_{i}^{2}, \text { for } D_{i}^{\prime \prime}>0, D_{i}^{\prime}<0 ; \\ \alpha_{x}+D_{i}^{\prime \prime} \cdot k_{i}^{2}-2 D_{i} \cdot k_{i}, \text { for } D_{i}^{\prime \prime}>0, \\ D_{i}>0,\end{array}\right.
where:
The proof of theorem 2 is similar to that of theorem 1. We notice that:
(33) quaddelta_(x)=x_(i)-x_(i-1)+x-x_(i-1) <= 2h_(i),quad i= bar(1,n)\quad \delta_{x}=x_{i}-x_{i-1}+x-x_{i-1} \leqslant 2 h_{i}, \quad i=\overline{1, n}.
Using the formulae (32) and (33), one obtains from the-- orems 1 and 2 the following results:
Theorem 3. In the conditions of theorem 1, the following estimate holds:
(34) ||( bar(H))-f^(-1)|| <= max{2h_(i),2h_(i)+p_(1),2h_(i)+p_(2),2h_(i)+p_(1)+p_(2)}\left\|\bar{H}-f^{-1}\right\| \leqslant \max \left\{2 h_{i}, 2 h_{i}+p_{1}, 2 h_{i}+p_{2}, 2 h_{i}+p_{1}+p_{2}\right\},
1 <= i <= n1 \leqslant i \leqslant n
where:
{:[p_(1)=-2min_(1 <= i <= n)D_(i-1)k_(i)],[p_(2)=-min_(1 <= i <= n)D_(i-1)^('')k_(i)^(2)]:}\begin{aligned}
& p_{1}=-2 \min _{1 \leqslant i \leqslant n} D_{i-1} k_{i} \\
& p_{2}=-\min _{1 \leqslant i \leqslant n} D_{i-1}^{\prime \prime} k_{i}^{2}
\end{aligned}
Theorem 4. In the conditions of theorem 2, the following estimate holds:
(35) ||( bar(H))-f^(-1)|| <= max_(1 <= i <= n){2h_(i),2h_(i)+ bar(p)_(1),2h_(i)+ bar(p)_(2),2h_(i)+ bar(p)_(1)+ bar(p)_(2)}\left\|\bar{H}-\mathrm{f}^{-1}\right\| \leqslant \max _{1 \leqslant \mathrm{i} \leqslant \mathrm{n}}\left\{2 h_{i}, 2 h_{i}+\bar{p}_{1}, 2 h_{i}+\bar{p}_{2}, 2 h_{i}+\bar{p}_{1}+\bar{p}_{2}\right\}.
1 <= i <= n1 \leqslant i \leqslant n
NUMERICAL APPLICATION
Based on the theory of Section 3, a calculation program written in BASIC (accuracy: 11 significant digits) was elaborated. This program allows to determine the root of an
equation f(x)=0f(x)=0, where the analytical expression of f(x)f(x) is known (case B, Section 1).
As input data one considers: n=n= the number of knots, and the knots ' x_(0),x_(1),dots,x_(n)x_{0}, x_{1}, \ldots, x_{n}. The values y_(i)=f(x_(i)),i= bar(0,n)y_{i}=f\left(x_{i}\right), i=\overline{0, n}, and those of the first and second order derivatives of these ones are computed by means of the numerical-type functions FWA(X), FNB(X), FNC(X), defined by the programmer for f(x),f^(')(x)f(x), f^{\prime}(x) and f^('')(x)f^{\prime \prime}(x), respectively.
The values f(x_(i)),i= bar(0,n)f\left(x_{i}\right), i=\overline{0, n}, are increasingly ordered (the lines 230-310230-310 of the program); after that, the values D_(0)^(')=l//f^(')(x_(0))D_{0}^{\prime}=l / f^{\prime}\left(x_{0}\right) and D_(0)^('')=-f^('')(x_(0))//(f^(')(x_(0)))^(3)D_{0}^{\prime \prime}=-f^{\prime \prime}\left(x_{0}\right) /\left(f^{\prime}\left(x_{0}\right)\right)^{3} are calculated. Then one determines D_(i)^(')D_{i}^{\prime} and D_(i)^(''),i= bar(l,n)D_{i}^{\prime \prime}, i=\overline{l, n}, with the recurrent relations (18).
After these calculations, the interval [y_(i),y_(i+1)],i== bar(0,n-1)\left[y_{i}, y_{i+1}\right], i= =\overline{0, n-1}, in which the root is lying, is tested, and then, by means of the formula (19), one estimates the value of the root bar(x)~= bar(H)(0)\bar{x} \cong \bar{H}(0) and f( bar(x))f(\bar{x}).
The program was especially applied to the case when only three knots, x_(0),x_(1),x_(2)x_{0}, x_{1}, x_{2}, are given (see Table 1), the root lying into one of the intervals (x_(0),x_(1)),(x_(1),x_(2))\left(x_{0}, x_{1}\right),\left(x_{1}, x_{2}\right).
In order to determine as accurately as possible the value of the root, the program contains a sequence which restricts the interval in which the knots are lying, as follows: if f(x_(0))*f( bar(x)) > 0f\left(x_{0}\right) \cdot f(\bar{x})>0, then one removes x_(0)x_{0}, while in the opposite case x_(n)x_{n} is removed; in both cases the value bar(x)\bar{x} found at the previous step is inserted instead of the removed knot. This successive removal process stops when |f( bar(x))||f(\bar{x})|
becomes smaller than 10^(-10)10^{-10} (this restriction being imposed by the restriction of the microcomputer we used).
Table 1 lists the results obtained by means of this program in the case of five equations. The listing of the program is given further down.
100 REM Inverse interpolating cubic spline for eq. solving.
110. DEF FHA (x)=4(x)=4. W x^3+3.7x^2+3.**x-1x^{\wedge} 3+3.7 x^{\wedge} 2+3 . * x-1.
130 DEF FNB (X)=12.3X^(4)2+6.3 X+3(X)=12.3 X^{4} 2+6.3 X+3.
30 DE FNC (X)=24.;X+6(X)=24 . ; X+6.
150 READ N LPEINTSPC (10)" Input Data" ILPRINT
160 DIM 170F0X(N+1),F(N+1),BP(N+1),BS(N+1)170 \mathrm{~F} 0 \mathrm{X}(\mathrm{N}+1), \mathrm{F}(\mathrm{N}+1), \mathrm{BP}(\mathrm{N}+1), \mathrm{BS}(\mathrm{N}+1)
180 FOR I=0 T0 H:READ X(I): NEXT I
180 FOR I=0I=0 TO N:AA=FNA(X(I)):F(I)=AA
NU (耓) =+訳."###"; I, F (I)
210 NEXT 1
220 LPRINT:LPRINT. SPC (4) "H(0)"; SPC(12)"f(H(0))"
230 REM Increasing arrangement of the values y(i)y(i).
240 IV=0
250 FOR I=0I=0 TO N-1
260 IF F(I)<=F(I+1)F(I)<=F(I+1) THEN GOTO 300 280 T=X(I):F(I)=F(I+1):F(I+1)=TT280 T=X(I): F(I)=F(I+1): F(I+1)=T T 290T=X(I):X(I)=X(I+1):X(I+1)=TT290 \mathrm{~T}=\mathrm{X}(\mathrm{I}): \mathrm{X}(\mathrm{I})=\mathrm{X}(\mathrm{I}+1): \mathrm{X}(\mathrm{I}+1)=\mathrm{TT}
300 NEXT I
310 IF NU<>0N U<>0 THEN GOTO 240
320 REM Values of DS(i) and DP (I). 330 DP(0)=1.//FNB(X(0))330 D P(0)=1 . / F N B(X(0))
340 DS ( 0 ) =-F=-F NC ( X(0)X(0) )/ (FNB ( X(0)X(0) )^3)
350 FOR I=0I=0 TO N-1N-1 360K=F(I+1)-F(I):H=X(I+1)-X(I)360 \mathrm{~K}=\mathrm{F}(I+1)-\mathrm{F}(I): \mathrm{H}=X(I+1)-X(I) 370DS(I+1)=6370 \mathrm{DS}(\mathrm{I}+1)=6. *** H/ (K**K)-2(\mathrm{K} * \mathrm{~K})-2. *** DS (I)-6(\mathrm{I})-6. *** DP (I)//K(\mathrm{I}) / \mathrm{K}
380 DP (I+1)=3(I+1)=3, H/K-KDS(I)/2.-2. *DP (I)
390 NEXT I
400 FOR I=0I=0 TO N-1N-1
410 IF F(I)~~F(I+1) < 0F(I) \approx F(I+1)<0. THEN GOTO 430
420 NEXT I 430quad Y=0.quad430 \quad Y=0 . \quad
440 AA=Y-F(I) 450BB=(DS(I+1)-DS(I))**AA^3//(F(I+1)-F(I))//6450 \mathrm{BB}=(D S(I+1)-D S(I)) * A A^{\wedge} 3 /(F(I+1)-F(I)) / 6.
470 (I) 470HY=BB+CC470 \mathrm{HY}=\mathrm{BB}+\mathrm{CC}
490 LPRINT USING " ^(**){ }^{*} ####### " ; HY ;
500 IF NN (HYN)
510 IF F (O) 540
520 FOR I=0I=0 TO (O). THEN X(N)=HYX(N)=H Y ELSE X(O)=HYX(O)=H Y
530 GOTB 230 N:AA=FNA(X(I)):F(I)=AA: NEXT I
S40 LPRINT:LP
550 LPRINT USING USING" ROOt: H(0)=+#\mathrm{H}(0)=+\#. ########"; HY
560 STOP
580 BATA 0.2,0.3,0.40.2,0.3,0.4
Note. We mention that a program was elaborated also for the inverse interpolating spline presented in Section 2 , but in many concrete cases, when the knots got near to the root (in the meaning of the above removal), undeterminacies appeared because of the small values of the denominator in formula (9). That is why the inverse interpolating spline proposed in Section 3 (which has not created such problems) has been chosen.
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