B-spline collocation methods were developed to provide simpler numerical solutions for differential problems. Over the years, various types of B-splines have been established, including the cubic B-spline collocation method (CBSM), cubic trigonometric B-spline collocation method (CTBSM), extended cubic B-spline collocation method (ECBSM), and cubic hybrid B-spline collocation method (CHBSM). Among these methods, CHBSM has been shown to produce the most accurate approximations due to the presence of a free parameter, γ, which allows for greater flexibility in the basis functions. However, the accuracy of the CHBSM is highly dependent on the value of γ, which must be optimized for improved results. While traditional brute-force optimization methods can achieve minimal errors, they often require significant computational time and effort. Therefore, this study has proposed using particle swarm optimization (PSO) to efficiently determine the optimal γ value for the CHBSM. The optimized CHBSM (OCHBSM) was tested on four examples of linear two-point boundary value problems (BVPs), including a linear BVP system. For comparison, the well-established CBSM and CTBSM were also applied to the same problems. The numerical results were analyzed and compared with analytical solutions revealing that the OCHBSM provided the most accurate approximations among the methods tested. Moreover, an average improvement percentage of 99.83% was achieved across all examples, indicating that our method outperforms the compared methods significantly.
Citation: Seherish Naz Khalid Ali Khan, Md Yushalify Misro. Hybrid B-spline collocation method with particle swarm optimization for solving linear differential problems[J]. AIMS Mathematics, 2025, 10(3): 5399-5420. doi: 10.3934/math.2025249
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B-spline collocation methods were developed to provide simpler numerical solutions for differential problems. Over the years, various types of B-splines have been established, including the cubic B-spline collocation method (CBSM), cubic trigonometric B-spline collocation method (CTBSM), extended cubic B-spline collocation method (ECBSM), and cubic hybrid B-spline collocation method (CHBSM). Among these methods, CHBSM has been shown to produce the most accurate approximations due to the presence of a free parameter, γ, which allows for greater flexibility in the basis functions. However, the accuracy of the CHBSM is highly dependent on the value of γ, which must be optimized for improved results. While traditional brute-force optimization methods can achieve minimal errors, they often require significant computational time and effort. Therefore, this study has proposed using particle swarm optimization (PSO) to efficiently determine the optimal γ value for the CHBSM. The optimized CHBSM (OCHBSM) was tested on four examples of linear two-point boundary value problems (BVPs), including a linear BVP system. For comparison, the well-established CBSM and CTBSM were also applied to the same problems. The numerical results were analyzed and compared with analytical solutions revealing that the OCHBSM provided the most accurate approximations among the methods tested. Moreover, an average improvement percentage of 99.83% was achieved across all examples, indicating that our method outperforms the compared methods significantly.
Throughout this paper, N and Q are the set of all positive integers and rational numbers, respectively, N0:=N∪{0},R+:=[0,∞). Moreover, for the set X, we denote n−times⏞X×X×⋯×X by Xn. For any l∈N0,m∈N, t=(t1,…,tm)∈{−1,1}m and x=(x1,…,xm)∈Vm we write lx:=(lx1,…,lxm) and tx:=(t1x1,…,tmxm), where ra stands, as usual, for the rth power of an element a of the commutative group V.
Let V be a commutative group, W be a linear space, and n≥2 be an integer. Recall from [15] that a mapping f:Vn⟶W is called multi-additive if it is additive (satisfies Cauchy's functional equation A(x+y)=A(x)+A(y)) in each variable. Some basic facts on such mappings can be found in [19] and many other sources, where their application to the representation of polynomial functions is also presented. Besides, f is said to be multi-quadratic if it is quadratic in each variable, i.e., it satisfies the quadratic equation
Q(x+y)+Q(x−y)=2Q(x)+2Q(y) | (1.1) |
in each variable [14]. In [27], Zhao et al. proved that the mapping f:Vn⟶W is multi-quadratic if and only if the following relation holds.
∑t∈{−1,1}nf(x1+tx2)=2n∑j1,j2,…,jn∈{1,2}f(x1j1,x2j2,…,xnjn) | (1.2) |
where xj=(x1j,x2j,…,xnj)∈Vn with j∈{1,2}.
The stability of a functional equation originated from a question raised by Ulam: “when is it true that the solution of an equation differing slightly from a given one must of necessity be close to the solution of the given equation?” (see [26]). The first answer (in the case of Cauchy's functional equation in Banach spaces) to Ulam's question was given by Hyers in [18]. Following his result, a great number of papers on the the stability problems of several functional equations have been extensively published as generalizing Ulam's problem and Hyers' theorem in various directions; see for instance [1,4,21,23,28], and the references given there.
It is worth mentioning that the fixed point theorems have been considered for various mappings, integral and fractional equations in [3,12,13]. Some investigations have been carried out on the stability of functional equations via fixed point theorems in [5,6,7,11]. Moreover, the fixed point theorem were recently applied to obtain similar stability results in [9,16,22,25].
In [14,15], Ciepliński studied the generalized Hyers-Ulam stability of multi-additive and multi-quadratic mappings in Banach spaces, respectively (see also [27]). Next, the stability of multi-Cauchy-Jensen mappings in non-Archimedean spaces are studied in [2] by applying the fixed point method, which was proved and used for the first time to investigate the Hyers-Ulam stability of functional equations in [11]. For more information about multi-quadratic, multi-cubic and multi-quartic mappings, we refer to [8,10,20,24].
In this paper, we define the generalized multi-quadratic mappings and present a characterization of such mappings. In other words, we reduce the system of n equations defining the generalized multi-quadratic mappings to obtain a single functional equation. Then, we prove the generalized Hyers-Ulam stability of multi-quadratic mapping (which was recently introduced by Salimi and Bodaghi in [24]) in non-Archimedean normed spaces by a fixed point method.
From now on, let V and W be vector spaces over Q, n∈N and xni=(xi1,xi2,…,xin)∈Vn, where i∈{1,2}. Let lj∈{1,2}. Put
Mni={x=(xl11,xl22,…,xlnn)∈Vn|Card{lj:lj=1}=i}. | (2.1) |
We shall denote xni and Mni by xi and Mi, respectively if there is no risk of ambiguity.
A general form of (1.1), say the generalized quadratic functional equation is as follows:
Q(ax+by)+Q(ax−by)=2a2Q(x)+2b2Q(y) | (2.2) |
where a,b are the fixed non-zero numbers in Q. The mapping f:Vn⟶W is said to be generalized n-multi-quadratic or generalized multi-quadratic if f is generalized quadratic in each variable.
Put n:={1,…,n}, n∈N. For a subset T={j1,…,ji} of n with 1≤j1<…<ji≤n and x=(x1,…,xn)∈Vn,
Tx:=(0,…,0,xj1,0,…,0,xji,0,…,0)∈Vn |
denotes the vector which coincides with x in exactly those components, which are indexed by the elements of T and whose other components are set equal zero. Note that ϕx=0, nx=x. We use these notations in the proof of upcoming lemma.
Let a∈Q be as in (2.2). We say the mapping f:Vn⟶W satisfies the r-power condition in the jth variable if
f(z1,…,zj−1,azj,zj+1,…,zn)=arf(z1,…,zj−1,zj,zj+1,…,zn), |
for all (z1,…,zn)∈Vn. In the sequel, (nk) is the binomial coefficient defined for all n,k∈N0 with n≥k by n!/(k!(n−k)!). We shall to show that if a mapping f:Vn⟶W satisfies the equation
∑q∈{−1,1}nf(ax1+qbx2)=2nn∑i=0a2ib2(n−i)∑x∈Mif(x), | (2.3) |
where a,b are the fixed non-zero in Q with a+b≠1, then it is generalized multi-quadratic quadratic. In order to do this, we need the next lemma.
Lemma 2.1. If the mapping f:Vn⟶W satisfies the Eq. (2.3) with 2-power condition in all variables, then f(x)=0 for any x∈Vn with at least one component which is equal to zero.
Proof. Putting x1=x2=(0,…,0) in (2.3), we get
2nf(0,…,0)=2nn∑i=0(ni)a2ib2(n−i)f(0,…,0)=2n(a+b)2nf(0,…,0). |
Since a+b≠1, f(0,…,0)=0. Letting x1k=0 for all k∈{1,…,n}∖{j} and x2k=0 for 1≤k≤n in (2.3) and using f(0,…,0)=0, we obtain
2na2f(0,…,0,x1j,0,⋯,0)=2nf(0,…,0,ax1j,0,…,0)=2na2n−1∑i=0(n−1i)a2ib2(n−1−i)f(0,…,0,x1j,0,…,0)=2na2(a+b)2(n−1)f(0,…,0,x1j,0,…,0). |
Hence, f(0,…,0,x1j,0,…,0)=0. We now assume that f(k−1x1)=0 for 1≤k≤n−1. We are going to show that f(kx1)=0. By assumptions, the above process can be repeated to obtain
2nf(kx1)=2na2kn−k∑i=0(n−ki)a2ib2(n−k−i)f(kx1)=2na2k(a+b)2(n−k)f(kx1), | (2.4) |
where 1≤k≤n−1 and so f(kx1)=0. This shows that f(x)=0 for any x∈Vn with at least one component which is equal to zero.
Theorem 2.2. Consider the mapping f:Vn⟶W. Then, the following assertions are equivalent:
(ⅰ) f is generalized multi-quadratic;
(ⅱ) f satisfies Eq. (2.3) with 2-power condition in all variables.
Proof. (ⅰ)⇒(ⅱ) We firstly note that it is not hard to show that f satisfies 2-power condition in all variables. We now prove that f satisfies Eq. (2.3) by induction on n. For n=1, it is trivial that f satisfies Eq. (2.2). Assume that (2.3) is valid for some positive integer n>1. Then,
∑q∈{−1,1}n+1f(axn+11+qbxn+12)=2a2∑q∈{−1,1}nf(axn1+qbxn2,x1n+1)+2b2∑q∈{−1,1}nf(axn1+qbxn2,x2n+1)=2n+1a2n∑i=0a2ib2(n−i)∑x∈Mnif(x,x1n+1)+2n+1b2n∑i=0a2ib2(n−i)∑x∈Mnif(x,x2n+1)=2n+1n+1∑i=0a2ib2(n+1−i)∑x∈Mn+1if(x). |
This means that (2.3) holds for n+1.
(ⅱ)⇒(ⅰ) Fix j∈{1,⋯,n}, put x2k=0 for all k∈{1,⋯,n}∖{j}. Using Lemma 2.1, we obtain
2n−1a2(n−1)[f(x11,…,x1j−1,ax1j+bx2j,x1j+1,…,x1n)+f(x11,⋯,x1j−1,ax1j−bx2j,x1j+1,⋯,x1n)]=2n−1[f(ax11,…,ax1j−1,ax1j+bx2j,ax1j+1,…,ax1n)+f(ax11,…,ax1j−1,ax1j−bx2j,ax1j+1,…,ax1n)]=2na2(n−1)[a2f(x11,…,x1j−1,x1j,x1j+1,…,x1n)+b2f(x11,…,x1j−1,x2j,x1j+1,…,x1n)]. | (2.5) |
It follows from relation (2.5) that f is quadratic in the jth variable. Since j is arbitrary, we obtain the desired result.
An special case of (2.2) is the following quadratic functional equation when a=b=12.
2Q(x+y2)+2Q(x−y2)=Q(x)+Q(y). | (3.1) |
A mapping f:Vn⟶W is called n-multi-quadratic or multi-quadratic if f is quadratic in each variable (see Eq. (3.1)). It is shown in [24, Proposition 2.2] (without extra 2-power condition in each variable) that a mapping f:Vn⟶W is multi-quadratic if and only if it satisfies the equation
2n∑q∈{−1,1}nf(x1+qx22)=∑l1,…,ln∈{1,2}f(xl11,xl22,…,xlnn). | (3.2) |
In this section, we prove the generalized Hyers-Ulam stability of Eq. (3.2) in non-Archimedean spaces.
We recall some basic facts concerning non-Archimedean spaces and some preliminary results. By a non-Archimedean field we mean a field K equipped with a function (valuation) |⋅| from K into [0,∞) such that |r|=0 if and only if r=0, |rs|=|r||s|, and |r+s|≤ max{|r|,|s|} for all r,s∈K. Clearly |1|=|−1|=1 and |n|≤1 for all n∈N.
Let X be a vector space over a scalar field K with a non-Archimedean non-trivial valuation |⋅|. A function ‖⋅‖:X⟶R is a non-Archimedean norm (valuation) if it satisfies the following conditions:
(ⅰ) ‖x‖=0 if and only if x=0;
(ⅱ) ‖rx‖=|r|‖x‖,(x∈X,r∈K);
(ⅲ) the strong triangle inequality (ultrametric); namely,
‖x+y‖≤max{‖x‖,‖y‖}(x,y∈X). |
Then (X,‖⋅‖) is called a non-Archimedean normed space. Due to the fact that
‖xn−xm‖≤max{‖xj+1−xj‖;m≤j≤n−1}(n≥m) |
a sequence {xn} is Cauchy if and only if {xn+1−xn} converges to zero in a non-Archimedean normed space X. By a complete non-Archimedean normed space we mean one in which every Cauchy sequence is convergent.
In [17], Hensel discovered the p-adic numbers as a number theoretical analogue of power series in complex analysis. The most interesting example of non-Archimedean normed spaces is p-adic numbers. A key property of p-adic numbers is that they do not satisfy the Archimedean axiom: for all x,y>0, there exists an integer n such that x<ny.
Let p be a prime number. For any non-zero rational number x=prmn in which m and n are coprime to the prime number p. Consider the p-adic absolute value |x|p=p−r on Q. It is easy to check that |⋅| is a non-Archimedean norm on Q. The completion of Q with respect to |⋅| which is denoted by Qp is said to be the p-adic number field. One should remember that if p>2, then |2n| = 1 in for all integers n.
Throughout, for two sets A and B, the set of all mappings from A to B is denoted by BA. The proof is based on a fixed point result that can be derived from [11, Theorem 1]. To present it, we introduce the following three hypotheses:
(H1) E is a nonempty set, Y is a complete non-Archimedean normed space over a non-Archimedean field of the characteristic different from 2, j∈N, g1,…,gj:E⟶E and L1,…,Lj:E⟶R+,
(H2) T:YE⟶YE is an operator satisfying the inequality
‖Tλ(x)−Tμ(x)‖≤maxi∈{1,…,j}Li(x)‖λ(gi(x))−μ(gi(x))‖,λ,μ∈YE,x∈E, |
(H3) Λ:RE+⟶RE+ is an operator defined through
Λδ(x):=maxi∈{1,…,j}Li(x)δ(gi(x))δ∈RE+,x∈E. |
Here, we highlight the following theorem which is a fundamental result in fixed point theory [11]. This result plays a key role in obtaining our goal in this paper.
Theorem 3.1. Let hypotheses (H1)-(H3) hold and the function ϵ:E⟶R+ and the mapping φ:E⟶Y fulfill the following two conditions:
‖Tφ(x)−φ(x)‖≤ϵ(x),liml→∞Λlϵ(x)=0(x∈E). |
Then, for every x∈E, the limit liml→∞Tlφ(x)=:ψ(x) and the function ψ∈YE, defined in this way, is a fixed point of T with
‖φ(x)−ψ(x)‖≤supl∈N0Λlϵ(x)(x∈E). |
Here and subsequently, given the mapping f:Vn⟶W, we consider the difference operator Γf:Vn×Vn⟶W by
Γf(x1,x2)=2n∑q∈{−1,1}nf(x1+qx22)−∑l1,…,ln∈{1,2}f(xl11,xl22,…,xlnn). |
In the sequel, S stands for {0,1}n. With these notations, we have the upcoming result.
Theorem 3.2. Let V be a linear space and W be a complete non-Archimedean normed space over a non-Archimedean field of the characteristic different from 2. Suppose that ϕ:Vn×Vn⟶R+ is a mapping satisfying the equality
liml→∞(1|2|2n)lmaxs∈Sϕ(2l(sx1,sx2))=0 | (3.3) |
for all x1,x2∈Vn. Assume also f:Vn⟶W is a mapping satisfying the inequality
‖Γf(x1,x2)‖≤ϕ(x1,x2) | (3.4) |
for all x1,x2∈Vn. Then, there exists a unique multi-quadratic mapping Q:Vn⟶W such that
‖f(x)−Q(x)‖≤supl∈N0(1|2|2n)l+1maxs∈Sϕ(2lsx,0) | (3.5) |
for all x∈Vn.
Proof. Replacing x=x1=(x11,…,x1n),x2=(x21,…,x2n) by 2x1,(0,…,0) in (3.4), respectively, we have
‖22nf(x)−∑s∈Sf(2sx)‖≤ϕ(2x,0) | (3.6) |
for all x∈Vn. Inequality (3.6) implies that
‖f(x)−Tf(x)‖≤ξ(x) | (3.7) |
for all x∈Vn, where ξ(x):=1|2|2nϕ(2x,0) and Tf(x):=122n∑s∈Sf(2sx). Define Λη(x):=maxs∈S1|2|2nη(2sx) for all η∈RVn+,x∈Vn. It is easy to see that Λ has the form described in (H3) with E=Vn, gi(x):=gs(x)=2sx for all x∈Vn and Li(x)=1|2|2n for any i. Moreover, for each λ,μ∈WVn and x∈Vn, we get
‖Tλ(x)−Tμ(x)‖≤maxs∈S1|2|2n‖λ(2sx)−μ(2sx)‖. |
The above inequality shows that the hypothesis (H2) holds. By induction on l, one can check that for any l∈N and x∈Vn that
Λlξ(x):=(1|2|2n)lmaxs∈Sξ(2lsx) | (3.8) |
for all x∈Vn. Indeed, by definition of Λ, equality (3.8) is true for l=1. If (3.8) holds for l∈N, then
Λl+1ξ(x)=Λ(Λlξ(x))=Λ((1|2|2n)lmaxs∈Sξ(2lsx))=(1|2|2n)lmaxs∈SΛ(ξ(2lsx))=(1|2|2n)l+1maxs∈Sξ(2l+1sx) |
for all x∈Vn. Relations (3.7) and (3.8) necessitate that all assumptions of Theorem 3.1 are satisfied. Hence, there exists a unique mapping Q:Vn⟶W such that Q(x)=liml→∞(Tlf)(x) for all x∈Vn, and also (3.5) holds. We are going to show that
‖Γ(Tlf)(x1,x2)‖≤(1|2|2n)lmaxs∈Sϕ(2lsx1,2lsx2) | (3.9) |
for all x1,x2∈Vn and l∈N. We argue by induction on l. For l=1 and for all x1,x2∈Vn, we have
‖Γ(Tf)(x1,x2)‖=‖2n∑q∈{−1,1}n(Tf)(x1+qx22)−∑l1,…,ln∈{1,2}(Tf)(xl11,xl22,…,xlnn)‖=‖12n∑q∈{−1,1}n∑s∈Sf(sx1+sqx2)−122n∑l1,…,ln∈{1,2}∑s∈Sf(2sxl11,2sxl22,…,2sxlnn)‖=‖122n∑s∈SΓ(f)(2(sx1,sx2))‖≤1|2|2nmaxs∈S‖Γ(f)(2(sx1,sx2))‖≤1|2|2nmaxs∈Sϕ(2(sx1,sx2)) |
for all x1,x2∈Vn. Assume that (3.9) is true for an l∈N. Then
‖Γ(Tl+1f)(x1,x2)‖=‖2n∑q∈{−1,1}n(Tl+1f)(x1+qx22)−∑l1,…,ln∈{1,2}(Tl+1f)(xl11,xl22,…,xlnn)‖=‖12n∑q∈{−1,1}n∑s∈STlf(sx1+sqx2)−122n∑l1,…,ln∈{1,2}∑s∈STlf(2sxl11,2sxl22,…,2sxlnn)‖=‖122n∑s∈SΓ(Tlf)(2(sx1,sx2))‖≤1|2|2nmaxs∈S‖Γ(Tlf)(2(sx1,sx2))‖≤(1|2|2n)l+1maxs∈Sϕ(2l+1(sx1,sx2)) | (3.10) |
for all x1,x2∈Vn. Letting l→∞ in (3.9) and applying (3.3), we arrive at ΓQ(x1,x2)=0 for all x1,x2∈Vn. This means that the mapping Q satisfies (3.2) and the proof is now completed.
The following example is an application of Theorem 3.2 concerning the stability of multi-quadratic mappings when the norm of Γf(x1,x2) is controlled by the powers sum of norms of components of vectors x1 and x2 in Vn.
Example 3.3. Let p∈R fulfills p>2n. Let V be a normed space and W be a complete non-Archimedean normed space over a non-Archimedean field of the characteristic different from 2 such that |2|<1. Suppose that f:Vn⟶W is a mapping satisfying the inequality
‖Γf(x1,x2)‖≤2∑k=1n∑j=1‖xkj‖p |
for all x1,x2∈Vn. Putting ϕ(x1,x2)=∑2k=1∑nj=1‖xkj‖p, we have ϕ(2lx1,2lx2)=|2|lpϕ(x1,x2) and so
liml→∞(1|2|2n)lmaxs∈S2∑k=1n∑j=1‖2lsxkj‖p=liml→∞(|2|p|2|2n)l2∑k=1n∑j=1‖xkj‖p=0 |
for all x1,x2∈Vn. On the other hand,
supl∈N(1|2|2n)l+1maxs∈Sϕ(2lsx,0)=1|2|2nn∑j=1‖x1j‖p. |
By Theorem 3.2, there exists a unique multi-quadratic mapping Q:Vn⟶W such that
‖f(x)−Q(x)‖≤1|2|2nn∑j=1‖x1j‖p |
for all x∈Vn.
Recall that a functional equation F is hyperstable if any mapping f satisfying the equation F approximately is a true solution of F. Under some conditions functional Eq. (3.2) can be hyperstable as follows.
Corollary 3.4. Suppose that pkj>0 for k∈{1,2} and j∈{1,…,n} fulfill ∑2k=1∑nj=1pkj>2n. Let V be a normed space and W be a complete non-Archimedean normed space over a non-Archimedean field of the characterisitic different from 2 such that |2|<1. If f:Vn⟶W is a mapping satifying the inequality
‖Γf(x1,x2)‖≤2∏k=1n∏j=1‖xkj‖pkj |
for all x1,x2∈Vn, then f is multi-quadratic.
The authors sincerely thank the anonymous reviewers for their careful reading, constructive comments and suggesting some related references to improve the quality of the first draft of paper.
The authors declare no conflicts of interest.
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