Applied Mathematics
Vol.08 No.09(2017), Article ID:78918,18 pages
10.4236/am.2017.89093
A Study of Weighted Polynomial Approximations with Several Variables (II)
Ryozi Sakai
Department of Mathematics, Meijo University, Tenpaku-ku, Nagoya, Japan
Copyright © 2017 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
http://creativecommons.org/licenses/by/4.0/
Received: August 13, 2017; Accepted: September 3, 2017; Published: September 6, 2017
ABSTRACT
In this paper we investigate weighted polynomial approximations with several variables. Our study relates to the approximation for
by weighted polynomial. Then we will give some results relating to the Lagrange interpolation, the best approximation, the Markov-Bernstein inequality and the Nikolskii-type inequality.
Keywords:
Weighted Polynomial Approximations, the Lagrange Interpolation, the Best of Approximation, Inequalities
1. Introduction
Let
(
times,
integer) be the direct product space, and let
, where
be even weight functions. We suppose that for every nonnegative integer n,
In this paper we will study to approximate the real-valued weighted function
by weighted polynomials
, where
. Here,
means a class of all polynomials with at most n-degree for each variable
. We need to define the norms. Let
, and let
be measurable. Then we define
We assume that for
the integral is independent of the order of integration with respect to each
. When
, we write
. If
, we require that f is continuous and
, where
. Then we write
.
Our purpose in this paper is to approximate the weighted function
by weighted polynomials
. In Section 2, we give a class of the weights which are treated in this paper. In Section 3, we state our main theorems. First, we consider the Lagrange interpolation polynomials. Next, we give the necessary and sufficient conditions for the best approximation. In Sections 4 and 5, we will prove theorems.
2. Class of Weight Functions and Preliminaries
Throughout the paper
denote positive constants independent of
or polynomials
. The same symbol does not necessarily denote the same constant in different occurrences. Let
mean that there exists a constant
such that
holds for all
, where
is a subset.
We say that
is quasi-increasing if there exists
such that
for
. Hereafter we consider following weights.
Definition 2.1. Let
be a continuous and even function, and satisfy the following properties:
(a)
is continuous in
, with
.
(b)
exists and is positive in
.
(c)
(d) The function
is quasi-increasing in
, with
(e) There exists
such that
Then we write
.
Moreover, if there also exists a compact subinterval
of
, and
such that
then we write
. If
is bounded, then the weight
is called a Freud-type weight, and if
is unbounded, then w is called an Erdös-type weight.
Let
,
and
be an integer. Then we write
if
is
-class and there exist
and
such that for all
,
(2.1)
and
for every
and also
Specific examples are shown in the following:
Example 2.2 (cf. [1] [2] ). (1) If an exponential
satisfies
where
are constants, then we call
the Freud weight. The class
contains the Freud weights.
(2) For
we define
where
. Moreover, we define
where
if
, and otherwise
. We note that
gives a Freud-type weight, that is,
is bounded..
(3) We define
(4) Let
, and let us define
If
, then we say that the weight w is regular. All weights in examples (1), (2) and (3) are regular.
(5) More generally we can give the examples of weights
. If the weight w is regular and if
satisfies definition (2.1), then for the regular weights we have
(see [3] , Corollary 5.5 (5.8)).
Proposition 2.3 ( [3] , Theorem 4.2 and (4.11)). Let m be a positive integer,
and let
. Then for
, we can construct a new weight
such that
and for some
,
where
and
are MRS-numbers for the weight
or
, respectively, and
are correspond for
or w, respectively.
Let
be orthonormal polynomials with respect to a weight w, that is,
is the polynomial of degree n such that
For
, we denote by
the usual
space on
(here for
, if
then we require
to be continuous, and
to have limit 0 at
). Let
. We need the Mhaskar-Rakhmanov-Saff numbers (MRS numbers)
;
we see easily
and
For
the degree of weighted polynomial approximation is defined by
3. Main Results
Let
, and let
, where
. Then we have the following theorem.
Theorem 3.1 ( [4] , Theorem 3.3). We suppose
,
and let
If
, then there exist
such that we have
First, we consider the Lagrange interpolation operators. We construct the orthonormal polynomials
with respect to the weight
for each
. Let
are zeros of the orthonormal polynomial
, that is,
and put
. Then for
we define the Lagrange interpolation polynomial on
as
(3.1)
where
(3.2)
In the rest of this paper, if
are the Freud-type weights then we suppose
.
Theorem 3.2. Let
, and let
be continuous. If
(3.3)
holds, then there exists
such that for
(3.4)
where for each
,
are zeros of
. In particular,
(3.5)
Theorem 3.3. Let
. Let
, and let
satisfy (3.3), then we have for
,
(3.6)
where for each
,
are zeros of
. In
particular, if
, then we
have
For
we also obtain the similar results. We need a function as follows:
(3.7)
Theorem 3.4. Let
. Let
and
, and let
be defined by (3.7) for each
. If
is continuous, and satisfies
(3.8)
then we have
(3.9)
Especially, if
satisfies
(3.10)
then we have
(3.11)
For
we have the following:
Theorem 3.5. Let
, and let satisfy
. Let
and
. Furthermore we assume
(3.12)
If
is continuous, and satisfies
(3.13)
then we have
Especially, by (3.13) we have
Remark 3.6. (1) We note that (3.13) means
where
(see Theorem 2.3).
(2) All examples in Example 2.2 hold (3.12).
(3) To prove Theorem 3.5 we use Proposition 4.5. Then Assumption (3.12) plays an important role.
Next, we characterize the best approximation polynomial (cf. [5] ).
Theorem 3.7. Let
. There is a best approximation polynomial
such that
Theorem 3.8 (Kolmogorov-type theorem).
is a best of approximation for a continuous function
with
, if and only if for each polynomial
,
(3.14)
where
denotes the set (which depends on
and
) of all points
for which
.
Theorem 3.9. Let
and
. Let
, where
be a linearly independent system satisfying
, and we consider polynomials
(3.15)
Let
. The polynomial
(3.16)
is a polynomial of the best approximation for
if and only if for every polynomial (3.15) the following equality (3.17) holds.
(3.17)
holds. If
, we also assume that
vanish only on a set of measure zero.
4. Proofs of Theorems 3.2, 3.3, 3.4 and 3.5
Lemma 4.1. Let
be zeros of the orthonormal polynomial
, and let
where
are coefficients. If for every
,
(4.1)
then we have
. Therefore, for
we have
(4.2)
Proof. Now we fix any
, and then we consider the polynomial
in
such that
Since
(see (4.1)), all coefficients of
equal to zero, that is,
(4.3)
Next, we fix any
and we consider
such that
Then by (4.3), we see
Hence,
If we continue this method inductively, then we have
(4.4)
We put
as
then from (4.4) we have
. Therefore, we conclude
that is,
. #
In the rest of this paper, we use the following notations:
We also use
Proposition 4.2 (cf. [6] , Theorem 1.2.2). Let
, and let
be an integer. Then for all
, we have
(4.5)
where
(4.6)
Proof. (see [6] , pp.12-13). Let
. From (3.1) and (4.2) we see
Hence we have
that is, (4.5) holds. Now, we see that (4.5) holds for any
. In fact, for
we set
Then
that is, (4.5) holds for any
. #
Lemma 4.3 ( [7] , Theorem 2.1). Let
. If w is a Freud-type weight, then we assume
. Then there exist constants
such that for every integer
,
Proof of Theorem 3.2.
by Lemma 4.3
Therefore we have (3.4). To prove (3.5) we use (3.4) and Proposition 4.2. For
we see
Now, we can take
as
(4.7)
In fact, when we put
(see Proposition 2.3), from (3.3) we see
Hence from Theorem 3.1 with
we have (4.7). Therefore we conclude (3.5). #
Proof of Theorem 3.3. By Proposition 4.2 with
and Theorem 3.2 with (3.4),
that is, we have (3.6). From Theorem 4.1 with
(note Proposition 2.3) and our assumption, there exists
such that
Then
#
We know the following propositions with respect to one variable.
Proposition 4.4 ( [8] , Theorem 2.7). Let
. Let
and
. If
is continuous, and satisfies
then we have
(4.8)
Especially, if
then we have
Proposition 4.5 ( [8] , Theorem 2.8). Let
, and let satisfy
. Let
and
. Furthermore we assume
If
satisfies
then we have
(4.9)
Especially, if
we have
Proof of Theorem 3.4. We use Proposition 4.4 (4.8).
Hence we have (3.9).
Next we show (3.10). There exists
such that
where
. Then
The last convergence follows from (3.9) and Theorem 3.1 with
(note Proposition 2.3). Consequently, we have (3.11). #
Proof of Theorem 3.5. As the proof of Theorem 3.4 we can show Theorem 3.5 by Proposition 4.5 (4.9). Then we also note Remark 3.6 (1) and (3). #
5. Proofs of Theorems 3.7, 3.8 and 3.9
In this section, we characterize the best approximation polynomial (cf. [5] ).
Proof of Theorem 3.7. We consider the polynomial class
Since
the set
is not empty. Now we select the sequence
such that
Here we see that
is bounded. In fact, if it is unbounded, then for
we see
. Then we can take a subsequence
and a fixed term
such that
We can suppose
as
(if we need it, then we consider a subsequence). Now, we see that there exists M > 0 such that
, so we have
that is,
This is impossible because the
are linear independent. Hence
is bounded. Now we repeat the method as above. If we select the sequence
as
(if we need it, then we consider a subsequence), then we have
Then we put
. #
Proof of Theorem 3.8. Let
where
. We see that the theorem is trivial if
. So we may assume
. If (3.14) is not true, there exists a polynomial
such that
for some
. By the continuity of the function, there exists an open subset
;
, such that
For
small enough we put
, and let
First, for
we see
If we take
, then we obtain
(5.1)
Next, we assume
(the complement of
). For large enough
there exists
such that
and
that is,
Then we also see that there exists
such that
Let
, and let
. Then, if we take
so small that
, we see
(5.2)
From (5.1) and (5.2) we see that the condition (3.14) is necessary.
Next we will show that (3.14) is also sufficient. Let
be arbitrary polynomial. Then there exists a point
such that for
,
Then we see
This means that there is not
with
, that is,
is the best of approximation polynomial. #
Proof of 3.9. Let the condition (3.17) be satisfied. We see
that is,
Hence
is the best approximation polynomial.
Next we give the converse assertion. We suppose (3.17). However if
, we also assume that
vanish only on a set of measure zero. (3.17) is equivalent to
for all
. Now we assume that for some
,
then it would be possible to find
so small on the basis of absolute magnitude that
But then
Consequently,
and we arrive at a contradiction on the assumption concerning the polynomial
. #
Cite this paper
Sakai, R. (2017) A Study of Weighted Polynomial Approximations with Several Variables (II). Applied Mathematics, 8, 1239-1256. https://doi.org/10.4236/am.2017.89093
References
- 1. Jung, H.S. and Sakai, R. (2009) Specific Examples of Exponential Weights. Communications of the Korean Mathematical Society, 24, 303-319.
https://doi.org/10.4134/CKMS.2009.24.2.303
- 2. Levin, A.L. and Lubinsky, D.S. (2001) Orthogonal Polynomials for Exponential Weights. Springer, New York. https://doi.org/10.1007/978-1-4613-0201-8
- 3. Sakai, R. and Suzuki, N. (2013) Mollification of Exponential Weights and Its Application to the Markov-Bernstein Inequality. The Pioneer Journal of Mathematics, 7, 83-101.
- 4. Sakai, R. A Study of Weighted Polynomial Approximations with Several Variables (I). Applied Mathematics. (Unpublished)
- 5. Timan, A.F. (1963) Theory of Approximation of Functions of a Real Variable. Pergamon Press, Oxford.
- 6. Mhaskar, H.N. (1996) Introduction to the Theory of Weighted Polynomial Approximation. World Scientific, Singapore.
- 7. Sakai, R. (2015) Quadrature Formula with Exponential-Type Weights. Pioneer Journal of Mathematics and Mathematical Sciences, 14, 1-23.
- 8. Jung, H.S. and Sakai, R. (2016) Lp-Convergence of Lagrange Interpolation Polynomials with Regular Symmetric Exponential Type Weight. Global Journal of Pure and Applied Mathematics, 12, 797-822.