The new schemes of calculation of double integrals and triple integrals are proposed in this paper. The formulas in which the double integral is converted into a line integral with respect to the arc length, and the triple integral is converted into a surface integral with respect to the area or a line integral with respect to the arc length are given separately. The effectiveness of the proposed methods is verified by several examples. Under certain conditions, these methods become the normal iterated integrals in Cartesian coordinate system or polar coordinate system, and the commonly used triple iterated integrals in Cartesian coordinate system, Cylindrical coordinate system or Spherical coordinate system. The transformation calculation method promoted in this paper points out the intrinsic relationship among double integral, triple integral, line integral and surface integral, which further enriches the theories of multivariate integrals.
Citation: Rong-jian Ning, Xiao-yan Liu, Zhi Liu. Conversion calculation method of multivariate integrals[J]. AIMS Mathematics, 2021, 6(3): 3009-3024. doi: 10.3934/math.2021183
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The new schemes of calculation of double integrals and triple integrals are proposed in this paper. The formulas in which the double integral is converted into a line integral with respect to the arc length, and the triple integral is converted into a surface integral with respect to the area or a line integral with respect to the arc length are given separately. The effectiveness of the proposed methods is verified by several examples. Under certain conditions, these methods become the normal iterated integrals in Cartesian coordinate system or polar coordinate system, and the commonly used triple iterated integrals in Cartesian coordinate system, Cylindrical coordinate system or Spherical coordinate system. The transformation calculation method promoted in this paper points out the intrinsic relationship among double integral, triple integral, line integral and surface integral, which further enriches the theories of multivariate integrals.
Regular two-level designs are widely used in factorial experiments. When the size of experimental units is large, inhomogeneity of experimental units has bad influences on estimating treatment effects (see, [1,2]). A useful way to reduce such bad influences is to block the experimental units into categories known as blocks. Thus, choosing optimal blocked regular two-level designs becomes an important issue.
As pointed out by [1], there are two kinds of blocking problems. One is called the single block variable problem which involves only one block variable and the other is called the multi block variables problem which considers two or more block variables. In the last decades, most of the literature were concerned with the single block variable problem. Sitter et al. [3], H. Chen and C. S. Cheng [4], R. C. Zhang and D. K. Park [5], and S. W. Cheng and C. F. J. Wu [6] respectively proposed different minimum aberration (MA) criteria which are suitable for selecting blocked designs with single block variable. Under these MA criteria, the construction methods of blocked designs with single block variable were discussed in [7,8,9].
Zhang et al. [10] proposed the general minimum lower-order confounding (GMC) criterion for choosing optimal regular two-level designs. The GMC criterion is preferable when there are some prior knowledge on the importance ordering of treatment effects. R. C. Zhang and R. Mukerjee [11] extended the GMC criterion to blocked designs with single block variable, referred as B-GMC criterion, and gave the construction methods of B-GMC blocked designs via complementary designs. From different considerations, [12] proposed another GMC criterion for blocked designs with single block variable, referred as B-GMC. Zhao et al. [13] and Zhao et al. [14] studied the construction methods of B-GMC designs. Zhang et al. [15] proposed multi-stage differential evolution algorithm for constrained -optimal design. Gashi [16] considered symmetric block design.
Compared to the experiments involving a single block variable, the experiments involving multi block variables are often encountered in practice. As has been mentioned in [1], in the agricultural context, when designs are laid out in rectangular schemes, both row and column inhomogeneity effects probably exist in the soil. Another example of multi block variables problem is from [2], which considers comparing two gasoline additives by testing them on two cars with two drivers over two days. In this experiment, three variables, cars, drivers and days, have to be considered to partition the experimental units.
Despite the wide application background, the multi block variables problem is less studied due to its complexity. In particular, constructing optimal designs with multi block variables is considerably challenging. Under the MA criterion, [17] developed some rules for constructing optimal regular two-level blocked designs with multi block variables. Zhang et al. [18] extended the idea of the GMC criterion to the case of multi block variables problem and developed the blocked GMC criterion, called B-GMC criterion. Inheriting the advantage of the GMC criterion, a B-GMC design is particularly preferable when some prior information on importance ordering of treatment effects is present. Zhang et al. [18] tabulated some B-GMC designs with small numbers of treatment factors and small run sizes by computer search. When or is large, computer search becomes computationally challenging, where . Zhao et al. [19] studied the B-GMC criterion and constructed a small number of B-GMC designs. In this paper, the B-GMC designs with the number of treatment factors all over are constructed. The construction results cover all that of [19]. The structures of the constructed B-GMC designs are concise and easy to implement.
The rest of the paper is organized as follows. Section 2 reviews the B-GMC criterion and introduces some notation. The construction methods of B-GMC designs are provided in Section 3. Section 4 gives concluding remarks. Some proofs are deferred to Appendix.
Let and . Denote the regular two-level saturated design as
in Yates order, where the columns , and are independent columns in the form of
where the superscript of each column denotes transpose, in the entry , followed by , is repeated times, and in the two successive entries 's, followed by two successive 's, are repeated times, and in the successive entries 's are followed by successive 's. The remaining columns in are generated by taking the component-wise products of any of the independent columns, where . For example, the column is generated by taking component-wise products of the independent columns and . Denote for , where and consists of the first columns of . Let for , then consists of the first columns of and consists of the last columns of .
Suppose that the inhomogeneity of the units of an experiment comes from different sources, i.e., block variables, denoted as . Suppose the block variable has levels, i.e., the units are grouped into blocks with respect to the block variable . Then there should be independent columns of to implement such a blocking schedule. Such columns are called block columns in the following. Denote as the collection of the independent block columns corresponding to the block variable . It is worth noting that the columns in have the following two relations:
the columns in are independent of each other for ;
a column from is not necessarily independent of the columns from , .
Clearly, when , the blocking problem with multi block variables reduces to that with a single block variable. For simplicity, we consider only the case of for . Then there are block columns and each of them blocks the experimental units into groups. Here, we would like to emphasize that the block columns are not necessarily independent of each other.
Throughout the paper, we use to denote a blocked regular design, where is a regular design, and consists of block columns. We will not differentiate block variables, block columns, and block factors in the following. B. Tang and C. F. J. Wu [20] introduced the concept of isomorphism which helps to narrow down the search for optimal blocked designs in this paper. An isomorphism is a one-to-one mapping from to such that for every . Two designs and are isomorphic if there exists an isomorphism that maps onto , and onto .
Zhang et al. [18] put forward the effect hierarchy principle for blocked designs with multi block variables as follows:
Lower order treatment factorial effects are more likely to be important than higher order ones, and treatment factorial effects of the same order are equally likely to be important.
Lower order block factorial effects are more likely to be important than higher order ones, and block factorial effects of the same order are equally likely to be important.
All the interactions between treatment and block factors are negligible.
With the effect hierarchy principle and weak assumption of effects involving three or more factors are usually not important and negligible, [18] proposed the B-GMC criterion which considers only the confounding among the main effects and two-factor interactions. As a common assumption in the blocking issues, if a treatment effect is confounded with a potentially significant block effect, the treatment effect cannot be estimated. Thus, confounding of the main effects of treatment factors with any potentially significant block effect is not allowed. In the following, we always suppose the main effects and the two-factor interactions of the block factors are potentially significant.
Denote as the number of main treatment effects which are aliased with two-treatment-factor interactions (tfi's) but not with any potentially significant block effects, where , . Similarly, denotes the number of tfi's which are aliased with the other tfi's but not with any potentially significant block effects. Denote
(2.1) |
A blocked design is called a B-GMC design if sequentially maximizes (2.1). Let be the number of -th order effects which are aliased with -th order effects of . Let
(2.2) |
A design is called a GMC design if sequentially maximizes (2.2).
Let
i.e., contains the potentially significant block effects. As aforementioned, confounding between main treatment effects and potentially significant block effects is not allowed which leads to , the empty set, and consequently , .
As a preparation of deriving B-GMC designs, we introduce one more piece of notation. For and , define
where denotes the cardinality of a set, and stands for the two-factor interaction of and . Thus, equals the number of tfi's of appearing in the alias set that contains .
To construct B-GMC designs, one should first consider the first part in (2.1), i.e., . Recall that for , then choosing to maximize reduces to choosing to maximize .
A design is said to have resolution if no -factor interaction is confounded with any other interaction involving less than factors (see, [21]). Note that a design with resolution at least IV has and for . This implies that a design with resolution at least IV must maximize . When , if has resolution at least IV, then (see, [22]). In the remaining part of this section, we suppose . By Lemma 1 in [13], to choose from , there are two possibilities: (i) , and (ii) . As has been pointed out, when constructing B-GMC designs, there should be . This leads to the constraint
(3.1) |
Certainly, for in the case (i), and thus satisfies the constraint (3.1). For in the case (ii), there must be resulting in the necessity to investigate the number of columns in . The following lemma addresses this question.
Lemma 1. Let be any -projection of with . If for some , then .
The proof of Lemma 1 is lengthy and thus deferred to Appendix.
Lemmas 2 and 3 below are straightforward extensions of some results in [19] and [23], respectively. These two lemmas are helpful in deriving the construction methods of B-GMC designs.
Lemma 2. Let be any -projection of with for some .
If , then and the equality holds when has independent columns.
If , then and the equality holds when has independent columns.
If , then .
If , then .
Lemma 3. Suppose consists of the last columns of . For any two columns and in , if is ahead of in Yates order, then .
Combining Lemmas 1, 2 and 3, the following theorem provides the constructions of B-GMC designs with , where .
Theorem 1. Suppose is a design with for some and for some . The design is a B-GMC design if consists of the last columns of and
is any -projection of when ,
is any -projection of when .
Proof. Let be a design with and . According to Lemma 1 of [22],
where . Note that has resolution at least IV, then is maximized by . Therefore, we consider only in (2.1) in the following.
For (i). From Theorem 2 of [22], is a GMC design and thus maximizes (2.2) among all . From Lemma 2 (iv), if is any -projection of , then . Suppose is the last column of in Yates order and , where . From Lemma 3, for any , we have . Since , we have for any . By the definition of , when , we have and
(3.2) |
From Lemma 3, for any , we have . Therefore, when , we have
(3.3) |
From (3.2), we obtain that sequentially maximizes
among all since is a GMC design.
Suppose is not a B-GMC design, then there exists a and some such that
(3.4) |
and
(3.5) |
Recall the definitions of and , we have
(3.6) |
where the second equality is due to for any . From (3.6), it is obtained that
(3.7) |
By (3.2), (3.3) and (3.7), it is obtained that
(3.8) |
Similarly, for we have
(3.9) |
From (3.2)–(3.5), we obtain
Then it leads to from Eqs (3.8) and (3.9). This contradicts Lemma 2 (ii) and completes the proof of (i).
For (ii). From Lemma 1, if , then which implies . This is not allowed as has been pointed out. Therefore, if , there should be . According to Lemma 2 (iii), if is an -projection of , then . The remainder of the proof is similar to that of (i) and omitted. This completes the proof.
In the following, an example is provided to illustrate the constructions of B-GMC designs with .
Example 1. Consider constructing B-GMC and designs. For both B-GMC designs to be constructed, we have , and as . The values of the parameters and satisfy . Therefore, to construct these two B-GMC designs, should be the last columns of .
For the case , we have which gives . Therefore, we should choose block columns according to Theorem 1 (i) as . Without loss of generality, let be the -projection of . Then is a B-GMC design.
For the case , we have which gives . Therefore, we should choose block columns according to Theorem 1 (ii) as . Without loss of generality, let be the -projection of . Then is a B-GMC design.
Similar to the discussion in the first paragraph of Section 3.1, when constructing B-GMC designs with , we should also first maximize . Suppose the number of columns in satisfies for some . According to the first paragraph of Section 3.2 in [22], when , if maximizes , then up to isomorphism. This implies that .
Suppose for some . According to Lemma 2 (i) and (ii) combined with Lemma 1, we have no matter or . Therefore, there should be with or , otherwise . For the case of with , it is trivial since and thus . This obtains that the design with and being any -projection of is a B-GMC design. The following theorem considers the constructions of B-GMC designs for the case of .
Theorem 2. Suppose is a design with for some and for some . The design is a B-GMC design if consists of the last columns of and is any -projection of .
Proof. Let be a design with . Then we have . From Lemma 3 of [22],
where .
By Lemma 2 (iv), if is any -projection of , then . Suppose is the last column of in Yates order and , where . From Lemma 3, if , then . By the definition of , when , we have and
(3.10) |
From Lemma 3, for any , we have . Therefore, when , we have
(3.11) |
Since consists of the last columns of , from Theorem 3 of [22], is a GMC design. Then sequentially maximizes (2.2). Recall that for . Thus, (3.10) implies that maximizes
among all .
Suppose is not a B-GMC design, then there exists a and some such that
and . With a similar argument to the proof of Theorem 1 (i), such a results in which contradicts Lemma 2 (i). This completes the proof.
In the following, an example is provided to illustrate the constructions of B-GMC designs with .
Example 2. Consider constructing B-GMC and designs.
For the case , we have , and as . Since , we obtain as . According to Theorem 3.2, let be the last columns of , and be a -projection of . Then is a B-GMC design.
For the case , we have , and as . Since , we obtain as . As discussed in the second paragraph in Section 3.2, let be the last columns of , and . Then is a B-GMC design.
Regular two-level factorial designs are widely used in factorial experiments. Inhomogeneity of the units has bad influences on estimating the treatment effects when size of experimental units is large. To reduce such bad influences, a useful way is to block the experimental units into categories. As has been pointed out in [1], there are two types of blocking problems. One is the single block variable problem and the other is the multi block variables problem. In the last decades, the single block variable problem was maturely investigated in the literature.
As has been exemplified in Section 1, multi block variables problem is more widely encountered in practice compared to the blocking problem with a single block variable. However, the studies on multi block variables problem are relatively primitive. The GMC criterion is welcome in the situations where the importance ordering of treatment effects is present. Zhang et al. [18] proposed the B-GMC criterion for choosing optimal blocked regular two-level designs. Construction methods on B-GMC designs can only be found in [19] in which the B-GMC designs of some from are constructed. In this paper, the B-GMC designs of all over are systemically constructed. The structures of the constructed B-GMC designs are concise and easy to implement.
This work was supported by the National Natural Science Foundation of China (Grant No. 11801331).
The authors declare that there is no conflict of interest.
We only need to prove for the case of . Recall that
We have
Denote and . Then , ,
and
where . Thus, it suffices to prove
(A.1) |
for .
Regarding to the columns in and , there are two cases:
(B1) , or
(B2) .
The global line of the remaining proof for Lemma 1 is as follows. In Lemma A.1, we first show that if (A.1) holds for the case (B1), then (A.1) holds for the case (B2). Afterwards, with Lemma A.2–A.5, we prove that (A.1) indeed holds for the case (B1).
Lemma A.1. Suppose that holds for the case , then holds for the case .
Proof. For the and in the case (B2), without loss of generality, we suppose . Then,
where and .
Note that , and , then and the case (B2) converts into the case (B1). Therefore, if (A.1) holds for the case (B1), then (A.1) holds for the case . This completes the proof.
In the following, we only need to prove that (A.1) holds for the case (B1).
For in the case (B1), we have . Then, has at least independent columns. We suppose has independent columns. Let denote the th independent column in in Yates order. Up to isomorphism, can be expressed as
(A.2) |
where has independent columns with and .
For in the case (B1), if , then and (A.1) holds. In the following, we consider only . Up to isomorphism, there are three cases for the columns in :
(C1) all the columns are from ;
(C2) some columns are from and the others are from ;
(C3) some columns are from , some are from and the others are from .
We first consider the case (C2) with . Note that . Denote . Then can be represented as
(A.3) |
where , and .
Recall that , we can always find a column or such that at least one column in , say , satisfies . Without loss of generality, we assume that there is some such that and . Meanwhile, there is some such that and .
Let and . Then,
(A.4) |
Let and . Then,
(A.5) |
Let
(A.6) |
and
(A.7) |
where and .
Lemma A.2. Suppose and are defined as in (A.4)–(A.7), respectively, then .
Proof. Let
Since and , we have . Thus
Since and are mutually exclusive, thus
Note that , then
Since and are mutually exclusive, thus
where the third equality is due to and . Therefore, to prove Lemma A.2, it suffices to prove
or equivalently
(A.8) |
Note that and , then
and
which implies that . Similarly, we can obtain . Therefore, (A.8) is equivalent to
(A.9) |
Thus, we only need to prove (A.9).
For the left hand side of (A.9), we have
For the right hand side of (A.9), we have
Since , we have . Then (A.9) holds. This completes the proof of Lemma A.2.
Remark 1. Lemma A.2 indicates that for any defined in (A.2) and defined in (A.3), we can always find , which has less columns out of than , and , which has no more columns out of than , such that . Repeatedly applying Lemma A.2, we can finally find and , which has no more columns out of than , such that .
For simplicity of notation, we still denote as and as . Then we can assume that . Note that there might be in the procedure above. Then, following Remark 1, has the following cases:
(D1) , or
(D2) ,
For the case (D2), we write as , where , and . Note that . We can always find a column or such that at least one column in , say , satisfies . Without loss of generality, suppose there is some with such that and . Denote and , then
(A.10) |
Denote
(A.11) |
where .
Lemma A.3. Suppose , and are defined as in (A.10) and (A.11), respectively, then .
Proof. Note that and . Therefore,
Since and , we have
where the third equality is due to . On one hand,
(A.12) |
On the other hand, , which leads to
(A.13) |
From (A.12) and (A.13), we obtain that
This implies and completes the proof.
Remark 2. By repeatedly applying Lemma A.3, we can finally find such that . This result is also true for the cases (C1) and (C3) with due to the following reasons. When , the case (C2) reduce to the case (C1). For the case (C3), with a similar argument to Lemma A.3, we can find a with and such that . Then the case (C3) reduces to case (C2).
The following remark considers the case of .
Remark 3. For , up to isomorphism, . In this situation, should equal to in the cases (C1), (C2) and (C3). Especially, in the cases (C1) and (C2), is already a subset of . By repeatedly applying Lemma A.3 to in the case (C3), we can find a set, say , such that and .
In summary, for any and defined in (A.2) and (A.3), by repeatedly applying Lemma A.2 and A.3, we can always find and with , such that . Next, we denote as and as and prove that for any , with and . We first introduce a useful lemma from [24].
Denote as the set consisting of the distinct columns generated by taking component-wise products of any two columns of .
Lemma A.4. Let be an -subset of , and , then , where the equality holds when the number of independent columns of is .
Lemma A.5. Suppose , , with and , then .
Proof. Without loss of generality, suppose is the th independent column in . Then and . Next, we show . Since , we have and has independent columns. By Lemma A.4, we have
(A.14) |
Note that then , and then . From (A.14), there should be and . This completes the proof.
Proof of Lemma 1. According to the proofs of Lemma A.2, A.3 and A.5, we can immediately obtain that . This completes the proof.
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1. | Yuna Zhao, Gengxin Sun, General Minimum Lower-Order Confounding Designs with Multi-Block Variables, 2021, 2021, 1563-5147, 1, 10.1155/2021/5548102 |