occurs ifffis a continuous function on the bounded and closed interval [x_(1),x_(2)x_{1}, x_{2}](x_(1) < x_(2))\left(x_{1}<x_{2}\right), differentiable on the open interval (x_(1),x_(2)x_{1}, x_{2}) andxi\xiis a convenient point of the latter interval. The pointxi\xidepends on the functionffbut the only indication that can be given about it, in general, is that it belongs to the interval (x_(1),x_(2)x_{1}, x_{2}). In fact, whatever it isc in(x_(1),x_(2))c \in\left(x_{1}, x_{2}\right), we can easily construct a functionffwhich meets the conditions imposed above for the validity of formula (1) and for whichccis the only possible value ofxi\xiHowever, in the case when the functionffbelongs to a particular set of functions, the position of the pointxi\xiit can, in certain cases, be made more precise by the existence of such a point in a certain particular subset of (x_(1),x_(2)x_{1}, x_{2}). In the following we will examine such problems for average formulas that generalize formula (1) of finite increments. 臨
2. Let us consider a real linear (hence additive and homogeneous) functionalR(f)R(f), defined on a linear setSSconsisting of real and continuous functionsff, defined over a given intervaland\mathbf{I}(of non-zero length) of the real axis. We will always assume thatSScontains all polynomials. The setSSmay coincide with the set of all continuous functionsf:IrarrRf: \mathbf{I} \rightarrow \mathbf{R}, but it can be even more restricted. In the following, when necessary, we will specify the setSSand the nature of its elements.
The degree of accuracy ofR(f)R(f)is a wholem >= -1m \geqq-1so thatR(f)R(f)cancels out on any polynomial of degreemmbut it is different from zero on the
at least a polynomial of degreem+1m+1The degree of accuracy may not exist, but if it does exist it is well determined and is characterized by the following property: {:[R(1)=0" if "m=-1","],[R(1)=R(x)=dots=R(x^(m))=0","quad R(x^(m+1))≒0" if "m >= 0.]:}\begin{gathered} R(1)=0 \text { if } m=-1, \\ R(1)=R(x)=\ldots=R\left(x^{m}\right)=0, \quad R\left(x^{m+1}\right) \fallingdotseq 0 \text { if } m \geqq 0 . \end{gathered}ăă
When necessary, we will further specify the nature of the linear functional.R(f)R(f). We recall the definition of the simplicity of the linear functionalR(f)R(f):
Linear functionalR(f)R(f)it is said to be in simple form if there is an integerm >= -1m \geqq-1, independent of functionff, so that for anyf in Sf \in Slet's have
whereKKis a non-zero constant independent of the functionffandxi_(w)nu==1,2,dots,m+2\xi_{w} \nu= =1,2, \ldots, m+2AREm+2m+2distinct points of the intervaland\mathbf{I}, dependinghat(generallydefunct)f\hat{in ~ general ~ of ~ function ~} f.
numbermmis completely determined and it is precisely the degree of accuracy ofR(f)R(f)We haveK=R(x^(m+1))K=R\left(x^{m+1}\right).
In formula (2) it is denoted by[y_(1),y_(2),dots,y_(r);f]\left[y_{1}, y_{2}, \ldots, y_{r} ; right]the divided difference, of the orderr-1r-1, of the functionffon the points, or nodes (distinct or11 hours11 hours)y_(1)y_{1},y_(2),dots,y_(r)y_{2}, \ldots, y_{r}
3. The theory of higher-order convex functions allows us to find different criteria for the simplicity of linear functionals .R(f)R(f)Such a criterion can be stated in the following form:
theorem 1. A necessary and sufficient condition for the linear functionalR(f)R(f), degree of accuracymm, to be of simple form is to haveR(f)!=0R(f) \neq 0for any functionf in Sf \in Sconvex of the ordermm.
A functionffis called convex by the ordermmonand\mathbf{I}if all its divided differences[x_(1),x_(2),dots,x_(m+2);f]\left[x_{1}, x_{2}, \ldots, x_{m+2} ; right], of the order ofm+1m+1, on distinct nodesx_(1)x_{1},x_(2),dots,x_(m+2)inIx_{2}, \ldots, x_{m+2} \in \mathbf{I}, are positive. If all these divided differences are nonnegative, the function is said to be nonconcave of ordermm(on 1). Finally, if the rents divided by the orderm+1m+1of the functionffare all n and all non-positive, this function is called concave, respectively the ordermm(on I). Moving from the functionffat the function-f-f, property of concave and non-convex functions of ordermmare generally deduced from the corresponding properties of convex and non-concave functions of ordermm. A convex (concave) function of ordermmis a particular case of a non-concave (non-convex) function of ordermmFor a function to be both non-concave and non-convex of ordermmit is necessary and
sufficient that all its differences divided by the orderm+1m+1, on distinct nodes, be equal to zero. Such a function is called a polynomial of ordermm(on I) and reduces to a polynomial of degreemm, more precisely, to the restriction onand\mathbf{I}of a polynomial of degreemm.
Let us proceed to an outline of the proof of Theorem 1.
Let us first show that the condition in the statement is necessary. Let us assume that the linear functionalR(f)R(f), degree of accuracymm, is of simple form. Letf in Sf \in Sa convex function of ordermm. We then have formula (2). whereK!=0K \neq 0. But, the difference divided by the second member is positive. So we haveR(f)!=0R(f) \neq 0. soR(f)!=0R(\mathrm{f}) \neq 0.
now that the condition in the statement is also sufficient. Let us suppose thatR(f)R(f)is of degree of accuracymmand is nonzero forend off \in \mathrm{~S}convex of the ordermmFunction
belongs toSSand a simple calculation shows us that we haveR(varphi)=0R(\varphi)=0It follows thatvarphi\varphiis not convex of ordermmIf we take into account the fact that -varphi\varphibelongs toSSand that we haveR(-varphi)=-R(varphi)=0R(-\varphi)=-R(\varphi)=0, it follows thatvarphi\varphiis not concave of the ordermm. There is thenm+2m+2distinct pointsxi_(nu)inI,nu=1,2,dots\xi_{\nu} \in \mathbf{I}, \nu=1,2, \ldots,m+2m+2so that we have
for any functionf in Sf \in Sconvex of the ordermmIndeed,x^(m+1)x^{m+1}is a convex function of ordermm, so ifffis convex of ordermmthe productR(x^(m+1))R(f)R\left(x^{m+1}\right) R(f)is different from zero. Suppose thatR(x^(m+1))R(f) < 0R\left(x^{m+1}\right) R(f)<0Then the functionR(x^(m+1))varphi=[R(x^(m+1))]^(2)fR(x^(m+1))R(f)x^(m+1)R\left(x^{m+1}\right) \varphi=\left[R\left(x^{m+1}\right)\right]^{2} f-R\left(x^{m+1}\right) R(f) x^{m+1}is (as a sum of two convex functions) a convex function of ordermmButR(R(x^(m+1))varphi)==R(x_(m+1))R(varphi)=0R\left(R\left(x^{m+1}\right) \varphi\right)= =R\left(x_{m+1}\right) R(\varphi)=0, which, based on Theorem 1 , is impossible. With this, inequality (4) is proven.
Under the same conditions ifffis a non-concave function of ordermmHAVE
Indeed, for anythingepsi > 0\varepsilon>0, functionf+epsix^(m+1)f+\varepsilon x^{m+1}is convex of ordermmand so we haveR(x^(m+1))R(f+epsix^(m+1))=R(x^(m+1))R(f)+epsi[R(x^(m+1))]^(2) > 0R\left(x^{m+1}\right) R\left(f+\varepsilon x^{m+1}\right)=R\left(x^{m+1}\right) R(f)+\varepsilon\left[R\left(x^{m+1}\right)\right]^{2}>0, whence, acting asepsi\varepsilonto tend to 0, inequality (5) is deduced.
For the properties of higher-order convex functions, for the notion of simplicity of a linear functional and for various other properties used in this paper, one can consult my previous works. For example, my paper in "Studii și Cercetări", Cluj [4].
Ifm >= 0m \geq 0it can even be said that the pointsxi,v=1,2,dots,m+2\xi, v=1,2, \ldots, m+2from formula (2) are within the intervalI\mathbf{I},
Ifm >= 0m \geqq 0, ifR(f)R(f)is the degree of accuracymmof simple form and ifffhas a derivativef^((m+1))f^{(m+1)}of the orderm+1m+1on the inside of itI\mathbf{I}, we have
(6)
wherexi\xiit is inside himI\mathbf{I}
Formulas (2) and (6) allow, in the case of simplicity, to delimit the functionalR(f)R(f)if the boundaries of the difference divided by the order are knownm+1m+1of the functionff, or its derivative of the orderm+1m+1, assumed to exist.
4. Suppose that the linear functionalR(f)R(f)is defined on the setSSof continuous functions on I and having a derivativef^((m+1))f^{(m+1)}of the orderm+1m+1on the inside of the door. We assume thatm >= 0m \geqq 0and thatR(f)R(f)is of the degree of accuracy given function inside itI\mathbf{I}, functional
is linear and vanishes on any polynomial of degreem+1m+1. Puttingf==x^(m+2)f= =x^{m+2}and taking into account (6), it is seen that there is a well-determined value^(c){ }^{c}(from the internal force it cancels out on any polynomial of grainm+2m+2. Numberccis given by equation
(8)
is defined onSSand it is of accuracym+2m+2
It is enough to show thatR_(1)(x^(m+3))R_{1}\left(x^{m+3}\right)is not equal to 0 .
Taking into account (8), we have
(10)R(x^(m+1))R_(1)(x^(m+3))=(1)/(2(m+2))[2(m+2)R(x^(m+1))R(x^(m+3))-(m+3)R^(2)(x^(m+2))]R\left(x^{m+1}\right) R_{1}\left(x^{m+3}\right)=\frac{1}{2(m+2)}\left[2(m+2) R\left(x^{m+1}\right) R\left(x^{m+3}\right)-(m+3) R^{2}\left(x^{m+2}\right)\right].
If we put
{:(11)P(x)=x^(m+3)+(m+3)zx^(m+2)+((m+2)(m+3))/(2)z^(2)x^(m+1):}\begin{equation*}
P(x)=x^{m+3}+(m+3) z x^{m+2}+\frac{(m+2)(m+3)}{2} z^{2} x^{m+1} \tag{11}
\end{equation*}
So we haveP^((m+1))(x) > 0P^{(m+1)}(x)>0forx!=-zx \neq-zIt follows that the polynomial (11) is convex of ordermm(everywhere). Based on inequality (4), we have
Lemma 1 follows from this.
We will see below that the linear functional (9) is of the simple form.
5. We will now assume that the interval I reduces to the bounded and closed interval[a,b](a < b)[a, b](a<b)and that the elementsffhis/hersSShave a continuous derivative of orderm+1m+1on[a,b][a, b].
We continue to assume thatm >= 0m \geqq 0.
Be it thenR(f)R(f)a linear functional defined onSS, degree of accuracymmand of simple form. Let us consider the linear functional (9), the numberccbeing determined by equation (8). We then havea < c < ba<c<b.
We have the following
Le ma 2. In addition to the previous hypotheses, if there is an integerk,0 <= k≤≦m+1k, 0 \leq k \leq \leqq m+1so that the linear functionalR(f)R(f)to be limited compared to the norm
So we haveR(x^(m+1))R_(1)(varphi_(m+3),lambda) >= 0R\left(x^{m+1}\right) R_{1}\left(\varphi_{m+3}, \lambda\right) \geqq 0, and, taking into account(12),R_(1)(x^(m+3))R_(1)(varphi_(m+3),lambda) >= 0(12), R_{1}\left(x^{m+3}\right) R_{1}\left(\varphi_{m+3}, \lambda\right) \geqq 0for anythinglambda\lambdabetweenaaandbb.
From theorem 15 of our cited work [4] it follows that the linear functionalR_(1)(f)R_{1}(f)is of simple form, so inequality (14) is true for any functionf in Sf \in Snon-concave of the orderm+2m+2(and even without equality possible ifffis convex of orderm+2m+2).
Lemma 2 is proven.
6. We can now prove the following
theorem 2. If the following assumptions are verified:
mmis a nonnegative integer.
SSis the set of functionsffhaving a continuous derivative of orderm+1m+1on the bounded and closed interval[a,b],(a < b)[a, b],(a<b).
R(f)R(f)is a linear functional defined onSS, of accuracy degree m, of simple form and bounded with respect to the norm (13) for a certain integerkkso that0 <= k <= m+10 \leqq k \leqq m+1.
ccis the point determined by equation (8) (We then havea < c < ba<c<b).
Functionffverify one of the following 4 properties:
A. is non-concave of orderm+1m+1and non-concave of the orderm+2m+2,
B. is nonconvex of orderm+1m+1and non-concave of the orderm+2m+2,
C. is non-concave of the orderm+1m+1and nonconvex of the orderm+2m+2.
D. is nonconvex of orderm+1m+1and nonconvex of the orderm+2m+2, then the average formula (6) is verified, in cases A and D, by at least one pointxi\xiof the interval[c,b][c, b]andhat(in)\hat{i n}cases B and C, by at least one pointxi\xiof the interval[a,c][a, c].
It is sufficient to do the proof in case A. In this case the function
is non-increasing on[a,b][a, b]and it cancels out at least one point inside the interval[a,b][a, b]So we haveg(a) >= 0,g(b) <= 0g(a) \geqq 0, g(b) \leq 0, and from Lemma 2 it follows that we also haveg(c) >= 0g(c) \geq 0. The property from the statement of the theorem follows. We can observe that the pointsxi\xiwhich verifies (6) forms an interval and the obtained property means that this interval has at least one point in common with [cb]. If, in particular, the functionffis convex of orderm+1m+1, the pointxi\xi, from formula (6) is unique and belongs to the interval[c,b][c, b].
otherwise cases D, C are deduced respectively from cases A, B by passing from the functionffto the function -f.
7. As a first application we have Gauss,
Consequence 1. IfR(f)R(f)is the remainder of the quadrature formula of type
{:(16)int_(a)^(b)f(x)dV(x)=sum_(v=1)^(n)lambda_(v)f(x_(v))+R(f):}\begin{equation*}
\int_{a}^{b} f(x) d V(x)=\sum_{v=1}^{n} \lambda_{v} f\left(x_{v}\right)+R(f) \tag{16}
\end{equation*}
where n is a natural number,VVa non-decreasing function, having at leastn+1n+1growing points andffa function that admits a continuous derivative of order2n2 non the bounded and closed interval [a,ba, b], average formula
is verified, in cases A, D of Theorem 2, for at least one point in the interval[c,b][c, b]and in casesB,C\mathrm{B}, \mathrm{C}of Theorem 2, for at least one pointxi\xiof the interval[a,c][a, c].
Here it was placedm=2n-1m=2 n-1andccis given by the corresponding equation (8).
In formula (16),x_(v),v=1,2,dots,nx_{v}, v=1,2, \ldots, nare the roots (distinct and located inside the interval[a,b][a, b]) of the orthogonal polynomial of degreennrelative to the distributiondV(x)d V(x). The numberslambda_(v),v=1,2,dots,n\lambda_{v}, v=1,2, \ldots, nare the coefficients (all> 0)>0)corresponding to Cristoffel.
This property can be generalized to more general Gaussian formulas by replacing the first term of formula (16) with a suitable inverse and nonnegative functional. Among these are those studied by us in a previous paper [3].
8. As another application of Theorem 2 , we have the following
Corollary 2. If the functionffis continuous and has a derivative of orderm+1m+1continues over an interval containing them+2m+2given pointsx_(v),nu=1,2,dots,m+2,nux_{v}, \nu=1,2, \ldots, m+2, n uall confused and wherem >= 0m \geq 0, then Cauchy's average formula,
is checked, in casesA,D\mathrm{A}, \mathrm{D}of Theorem 2, for at least one pointxi >= (1)/(m+2)sum_(v=1)^(m+2)x_(v)\xi \geqq \frac{1}{m+2} \sum_{v=1}^{m+2} x_{v}, and in cases B, C of Theorem 2, for at least one pointxi <= (1)/(m+2)sum_(v=1)^(m+2)x_(v)\xi \leqq \frac{1}{m+2} \sum_{v=1}^{m+2} x_{v}.
Divided difference[x_(1),x_(2),dots,x_(m+2);f]\left[x_{1}, x_{2}, \ldots, x_{m+2} ; f\right]where the nodesx_(v),v=1,2,dotsx_{v}, v=1,2, \ldots,m+2m+2are distinct ornun u, is defined as usual.
It is seen that the linear functionalR(f)=[x_(1),x_(2),dots,x_(m+2);f]R(f)=\left[x_{1}, x_{2}, \ldots, x_{m+2} ; f\right]verifies all the hypotheses in theorem 2 (provided that the pointsx_(y)x_{\mathrm{y}}, so that they are not all confused),[a,b][a, b]being an interval containing all the nodesx_(v),v==1,2,dots,m+2x_{v}, v= =1,2, \ldots, m+2In this case the pointccis precisely the arithmetic mean(1)/(m+2)sum_(v=1)^(m+2)x_(v)\frac{1}{m+2} \sum_{v=1}^{m+2} x_{v}of the nodes.
Form=0m=0we obtain the corresponding properties relative to the finite growth formula (1). It is unnecessary to state these properties here.
9. The property expressed by consequence 2 can also be demonstrated directly in the following way. To fix the ideas, let us assume that we are in case A, so that the functionffis non-concave of the orderm+1m+1and non-concave of the orderm+2m+2. Reasoning as was done on the function (15) for the proof of Theorem 2 and using some well-known formulas on divided differences, we have first, assumingx_(1) <= x_(2) <= dots <= x_(m+2)x_{1} \leqq x_{2} \leqq \ldots \leqq x_{m+2},
as we have demonstrated in another paper [2].
Corollary 2 now follows immediately.
10. The property expressed by consequence 1 follows from that expressed by consequence 2. Indeed, from some formulas that we have established elsewhere [1], it follows that the restR(f)R(f)of Gauss's formula (16) differs only by a positive constant factor from the difference divided by the order2n2 nof the functionffwith the nodes in the roots of orthogonal polynomials of degreennandn+1n+1.
In some cases, it is possible to proceed differently. In particular, eitherV=xV=x. Thenx_(v),v=1,2,dots,nx_{v}, v=1,2, \ldots, nare the roots of the polynomial
BecauseR(f)R(f)is a linear functional of degree of accuracy2n-12 n-1,R^(**)(F)R^{*}(F)is a linear functional of degree of accuracy2n2 n, so nut differs only by a constant (positive) factor from the divided difference of the functionFFon the nodesa,b,x_(v),v=1,2,dots,na, b, x_{\mathrm{v}}, v=1,2, \ldots, nLATESTnneach being taken twice. It is easy to see that
R(f)R(f)est une fonctionnelle lineare defined sur l'ensemble des fonctionsffayant une dérivée continuous d'ordrem+1(m >= 0)m+1(m \geq 0)sur l'intervale borné et fermé[a,b](a < b)[a, b](a<b)AndR(f)R(f)is the degree of accuracymm, from simple forms et est bornée par rapport à une norme de la forms (13), alors la formula de la moyenne (6) est verificie pour au moins un pointxi\xiof[c,b][c, b]respectively of[a,c][a, c], whereccis the point of(a,b)(a, b)given by (8) et suivant que 1a fonctionffverify en même temps, dans un ordre determined par le théorème 2, des properties de non-concavité et de non-convexité d'ordrem+1m+1and orderm+2m+2.
BIBLIOGRAPHY
[1] Popoviciu, T., Notes sur les fonctions convexes d'ordre supérieur (IV). Disquisitiones Math. et Physicae, I, 163-171 (1940).
[2] - Notes sur les fonctions convexes d'ordre supérieur (V). Bulletin de l'Acad. Rou-
[3] maine, XX1, 351-356 (1940). of Gauss's numerical intergrave. Studies and [3] Circle. Scientific Iasi, VI,29-5729-57(1955).
[4] - On the remainder in some linear approximation formulas of analysis. Studii și Cerc. de Matematică (Cluj) X, 337-389 (1959).
Received on 2. XII. 1971.
This paper is a slightly modified version of a paper published in French in Spisy prirodov. fak.Univ. JE Purkyne v. Brne, 5, 147-156 (1969).