
The Covid-19 pandemic has wrought significant challenges on both national and regional economic systems, resulting in an exacerbation of poverty and a deceleration of economic growth. These repercussions have led to increased social tensions and discontent.
Tuscany a key player in the Italian and European economies, warrants scrutiny concerning its economic structure post-pandemic, particularly in relation to the role played by industry districts and the ways for it to get out of the crisis and get back on track.
This paper undertakes an analysis of the strengths and weaknesses of the Tuscan economy, with a focus on EMAS-APO industry districts. It examines the region's standing in terms of national and European ERCI competitiveness, placing it at an average level. Additionally, the region ranks in the third decile of the DESI digitalization, indicating a commendable technological dynamism. An equally satisfying spending power in R&D, in percentage of GDP higher than the national one, and ranking 6th in the regions' rank, is flanked by a particularly vigorous innovation capacity of the production sector.
Employing a symmetric branch-by-branch SAM 2019 analysis, derived from a sector-by-product 2014 SAM constructed by IRPET, serves as the foundational step in understanding the interventions and investments necessary for resuming growth.
While affirming Tuscany's competitiveness, digitalization and technological dynamism, the analysis underscores the pivotal role of manufacturing. Notably, the textile, fashion and leather sectors hold significant weight, along with the slightly less impactful paper and pharmaceutical sectors, and the noteworthy role of tourism. The region emerges as innovative, competitive and export-oriented, albeit with certain sectors grappling with environmental challenges.
The IMM based analysis reveals a satisfactory average integration degree of the production system, suggesting that, for an effective, robust and sustainable post-Covid recovery and economic resurgence, focused investments and demand stimulus policy should be directed towards the textile, fashion and leather industries. Additionally, there is a strong indication to channel investments into pharmaceuticals and health care, and on transportation (especially freight transportation), as well as wholesale and retail trade. While weaker, there is a notable suggestion to invest in metallurgy, computer hardware, tourism and paper industries.
Citation: Guido Ferrari, Yanyun Zhao, José Mondéjar Jiménez, Luca Secondi. The economy of Tuscany in the post Covid-19 era: struggling with energy crisis and inflation. What to do to resume the journey?1[J]. National Accounting Review, 2024, 6(1): 1-26. doi: 10.3934/NAR.2024001
[1] | Mahmoud Abdallat, Abdallah Barjas Qaswal, Majed Eftaiha, Abdel Rahman Qamar, Qusai Alnajjar, Rawand Sallam, Lara Kollab, Mohammad Masa'deh, Anas Amayreh, Hiba Mihyar, Hesham Aboushakra, Bayan Alkelani, Rawan Owaimer, Mohannad Abd-Alhadi, Salwa Ireiqat, Fahed Turk, Ahmad Daoud, Bashar Darawsheh, Ahmad Hiasat, Majd Alhalaki, Shahem Abdallat, Salsabiela Bani Hamad, Rand Murshidi . A mathematical modeling of the mitochondrial proton leak via quantum tunneling. AIMS Biophysics, 2024, 11(2): 189-233. doi: 10.3934/biophy.2024012 |
[2] | Delores J. Grant, Leighcraft A. Shakes, Hope M. Wolf, Derek C. Norford, Pradeep K. Chatterjee . Exploring function of conserved non-coding DNA in its chromosomal context. AIMS Biophysics, 2015, 2(4): 773-793. doi: 10.3934/biophy.2015.4.773 |
[3] | Mario P. Carante, Francesca Ballarini . Modelling cell death for cancer hadrontherapy. AIMS Biophysics, 2017, 4(3): 465-490. doi: 10.3934/biophy.2017.3.465 |
[4] | Mohammed I. A. Ismail, Abdallah Barjas Qaswal, Mo'ath Bani Ali, Anas Hamdan, Ahmad Alghrabli, Mohamad Harb, Dina Ibrahim, Mohammad Nayel Al-Jbour, Ibrahim Almobaiden, Khadija Alrowwad, Esra'a Jaibat, Mira Alrousan, Mohammad Banifawaz, Mohammed A. M. Aldrini, Aya Daikh, Nour Aldarawish, Ahmad Alabedallat, Ismail M. I. Ismail, Lou'i Al-Husinat . Quantum mechanical aspects of cardiac arrhythmias: A mathematical model and pathophysiological implications. AIMS Biophysics, 2023, 10(3): 401-439. doi: 10.3934/biophy.2023024 |
[5] | E.N. Baranova, R.M. Sarimov, A.A. Gulevich . Stress induced «railway for pre-ribosome export» structure as a new model for studying eukaryote ribosome biogenesis. AIMS Biophysics, 2019, 6(2): 47-67. doi: 10.3934/biophy.2019.2.47 |
[6] | Andrea Bianchi, Chiara Lanzuolo . Into the chromatin world: Role of nuclear architecture in epigenome regulation. AIMS Biophysics, 2015, 2(4): 585-612. doi: 10.3934/biophy.2015.4.585 |
[7] | Michèle Siek, Berenice Marg, Chris M. Ehring, Derya Kirasi, Michael Liebthal, Thorsten Seidel . Interplay of vacuolar transporters for coupling primary and secondary active transport. AIMS Biophysics, 2016, 3(4): 479-500. doi: 10.3934/biophy.2016.4.479 |
[8] | Arjun Acharya, Madan Khanal, Rajesh Maharjan, Kalpana Gyawali, Bhoj Raj Luitel, Rameshwar Adhikari, Deependra Das Mulmi, Tika Ram Lamichhane, Hari Prasad Lamichhane . Quantum chemical calculations on calcium oxalate and dolichin A and their binding efficacy to lactoferrin: An in silico study using DFT, molecular docking, and molecular dynamics simulations. AIMS Biophysics, 2024, 11(2): 142-165. doi: 10.3934/biophy.2024010 |
[9] | Anuroop Venkateswaran Venkatasubramani, Katy McLaughlin, Giovanny Rodriguez Blanco, Vladimir Larionov, Alexander Kagansky . Pilot RNAi screening using mammalian cell-based system identifies novel putative silencing factors including Kat5/Tip60. AIMS Biophysics, 2015, 2(4): 570-584. doi: 10.3934/biophy.2015.4.570 |
[10] | Joshua Holcomb, Nicholas Spellmon, Yingxue Zhang, Maysaa Doughan, Chunying Li, Zhe Yang . Protein crystallization: Eluding the bottleneck of X-ray crystallography. AIMS Biophysics, 2017, 4(4): 557-575. doi: 10.3934/biophy.2017.4.557 |
The Covid-19 pandemic has wrought significant challenges on both national and regional economic systems, resulting in an exacerbation of poverty and a deceleration of economic growth. These repercussions have led to increased social tensions and discontent.
Tuscany a key player in the Italian and European economies, warrants scrutiny concerning its economic structure post-pandemic, particularly in relation to the role played by industry districts and the ways for it to get out of the crisis and get back on track.
This paper undertakes an analysis of the strengths and weaknesses of the Tuscan economy, with a focus on EMAS-APO industry districts. It examines the region's standing in terms of national and European ERCI competitiveness, placing it at an average level. Additionally, the region ranks in the third decile of the DESI digitalization, indicating a commendable technological dynamism. An equally satisfying spending power in R&D, in percentage of GDP higher than the national one, and ranking 6th in the regions' rank, is flanked by a particularly vigorous innovation capacity of the production sector.
Employing a symmetric branch-by-branch SAM 2019 analysis, derived from a sector-by-product 2014 SAM constructed by IRPET, serves as the foundational step in understanding the interventions and investments necessary for resuming growth.
While affirming Tuscany's competitiveness, digitalization and technological dynamism, the analysis underscores the pivotal role of manufacturing. Notably, the textile, fashion and leather sectors hold significant weight, along with the slightly less impactful paper and pharmaceutical sectors, and the noteworthy role of tourism. The region emerges as innovative, competitive and export-oriented, albeit with certain sectors grappling with environmental challenges.
The IMM based analysis reveals a satisfactory average integration degree of the production system, suggesting that, for an effective, robust and sustainable post-Covid recovery and economic resurgence, focused investments and demand stimulus policy should be directed towards the textile, fashion and leather industries. Additionally, there is a strong indication to channel investments into pharmaceuticals and health care, and on transportation (especially freight transportation), as well as wholesale and retail trade. While weaker, there is a notable suggestion to invest in metallurgy, computer hardware, tourism and paper industries.
Nonlinear problems are a hot topic in modern mathematics, attracting the attention of many researchers. Problems in fields such as economics, physics, chemistry, and fluid dynamics can be transformed into nonlinear equation systems. The solution of nonlinear equation systems is difficult to discover, and this problem can be solved using some numerical methods [1,2,3]. Chen et al. proposed the alternating direction implicit (ADI) compact differential scheme, which reduces the CPU time of two-dimensional problems [4]. Yang et al. proposed an efficient compact finite difference method [5,6,7] for solving nonlinear equations and an orthogonal Gauss collocation method (OGCM) [8] for solving systems of partial differential equations.
Therefore, in-depth research on numerical methods for solving nonlinear systems has important practical significance [9]. In solving nonlinear systems, we can obtain approximate solutions with the help of various iterative methods. Multi-point iterative methods [10,11,12,13] are an effective class of methods for solving nonlinear equation systems.
Wang et al. achieved research results in the study of multi-point iterative methods, such as local convergence analysis [14,15,16] and fractal theory-based investigations of iterative methods[17,18]. Cordero et al. studied the three-step iterative method [19,20] for solving nonlinear equations.
Newton's iterative method [21] is one of the most famous iterative methods
x(n+1)=x(n)−S′(x(n))−1S(x(n)),n∈0,1,2,⋯. | (1.1) |
where S:D⊆Rn⟶Rn and the inverse of the Jacobian matrix S′(xn) is S′(xn)−1.
On the basis of Newton's method, Potra-Pták [22] proposed a third-order iterative method
{yn=xn−s(xn)s′(xn),xn+1=xn−s(xn)+s(yn)s′(xn). | (1.2) |
These methods (1.2) have the advantage of not needing to compute second-order derivatives.
Cordero et al.[23] proposed an iterative method using division difference instead of the traditional Jacobi matrix
Sn+1=Sn−[m∑h=1AhJS(ηh(Sn))]−1F(Sn), | (1.3) |
where ηh(x)=x−τhJ−1S(x)S(x) and the parameters Ah satisfy ∑mh=1Ah=1. Considering τ1=0, the following iterative method is obtained:
Sn+1=Sn−[14JS(xn)+34JS(xn−23JS(xn)−1S(xn))]−1S(xn). | (1.4) |
Kou et al. [24] proposed an iterative method for solving nonlinear equations with convergence of at least third order
{yn=xn−s(xn)s′(xn),xn+1=xn−γs(xn)+s(yn)s′(xn)−(1−γ)s(xn)2s′(xn)(s(xn)−s(yn)), | (1.5) |
where γ∈R. Let S[xn,yn]=S(yn)−S(xn)yn−xn and S(xn)2S′(xn)(s(xn)−S(yn)) can be represented asS(xn)S[xn,yn]. We obtain an iterative method (1.6) for solving nonlinear systems. The iterative method (1.5) can be represented as
x(n+1)=x(n)−γS′(x(n))−1(S(x(n))+S(y(n)))−(1−γ)S[x(n),y(n)]−1S(x(n)), | (1.6) |
where
y(n)=x(n)−S′(x(n))−1S(x(n)), |
γ∈R is a real parameter.
The contents of this paper are as follows: Section 2 analyzes the order of convergence of the extended iterative method (1.6). In Section 3, we adopt the Möbius conjugacy map to convert the iterative method (1.6) to a rational operator and examine the fixed points and critical points for this rational operator in depth. The parameter space is constructed and some specific parameters are chosen in the parameter space. Section 4 compares the iterative method (1.6) with other iterative methods through numerical experiments. Section 5 shows some fractal diagrams. We verify that the expanded method has optimal stability and convergence for the parameter γ=0.
We prove the convergence order for the iterative method (1.6) with the following theorem.
Theorem 1. Let S:D⊆Rn→Rn be a sufficiently differentiable function from an open convex set D and let x∗∈D denote the solution of S(x)=0 that makes S′ continuous and nonsingular at x∗. The error equation of the iterative method (1.6) is
e(n+1)=c22(1+γ)(e(n))3+(−3c32(1+2γ)+c2c3(3+4γ))(e(n))4+O((e(n))5). | (2.1) |
When the parameter γ=−1, the iterative method is fourth-order convergent and the error expression is
e(n+1)=(3c32−c2c3)(e(n))4+O((e(n))5). | (2.2) |
Proof. Let C(n)=1n!S′(x∗)−1S(n)(x∗),i≥2 and e(n)=x(n)−x∗. Expanding S in terms of the Taylor's series on x∗, we get
S(x(n))=S′(x∗)(e(n)+C2(e(n))2+C3(e(n))3+C4(e(n))4+C5(e(n))5)+O((e(n))6), | (2.3) |
S′(x(n))=S′(x∗)(I+2C2e(n)+3C3(e(n))2+4C4(e(n))3+5C5(e(n))4)+O((e(n))5). | (2.4) |
According to the equation S′(x(n))−1S′(x(n))=I, we can calculate the inverse of S′(x(n)) :
S′(x(n))−1=S′(x(∗))−1(I+A1e(n)+A2(e(n))2+A3(e(n))3+A4(e(n))4)+O((e(n))5)), | (2.5) |
where
A1=−2C2,A2=4C22−3C3,A3=−8C32+6C2C3+6C3C2−4C4,A4=16C42+9C23+8C2C4+8C4C2−12C22C3−12C2C3C2−12C3C22−5C5. |
Now, using(2.3)–(2.5), we get
y(n)−x∗=x(n)−x∗−S′(x(n))−1S(x(n))=e(n)+C2(e(n))2+(2C3−2C22)(e(n))3+(3C4−4C2C3−3C2C3+4C32)(e(n))4+O((e(n))5). | (2.6) |
Taylor expansion of S(y(n)) at x∗ gives
S(y(n))=S(x∗)+S′(x∗)(y(n)−x∗)+S″(x∗)2!(y(n)−x∗)2+⋯+S(n)(x∗)n!(y(n)−x∗)n, | (2.7) |
and we have
S(y(n))=S′(x(n))(C2(e(n))2−2(C22−C3)(e(n))3+(5C32−7C2C3+3C4)(e(n))4)+O((e(n))5). | (2.8) |
In the iterative method (1.6), a first-order difference quotient operator is introduced, which can be regarded as a mapping relationship S[x,y]:D×D⊂Rn×Rn→L(Rn),
S[x,y]=∫10S′(x+ρd)dρ,∀(x,d)∈Rn×Rn. |
By performing Taylor expansion on S′(x+ρd) at point x, we can obtain the following results:
∫10S′(x+ρd)dρ=S′(x)+12S′′(x)d+16S′′′(x)d2+O(d3). | (2.9) |
According to
x+d=y,d=y−x=−S′(x(n))−1S(x(n)), | (2.10) |
the following conclusion can be reached:
S[x(n),y(n)]=S′(x∗)(I+U1e(n)+U2(e(n))2+U3(e(n))3+U4(e(n))4+O((e(n))5), | (2.11) |
where
U1=C2,U2=C22+C3,U3=−2C23+3C2C3+C4,U4=4C24−8C22C3+2C32+4C2C4+C5. | (2.12) |
Based on the equation S[x(n),y(n)]−1S[x(n),y(n)]=I, the inverse of S[x(n),y(n)]−1 is
S[x(n),y(n)]−1=S′(x∗)−1(I−U1e(n)+(U21−U2)(e(n))2+(−U31+2U1U2−U3)(e(n))3+O((e(n))4)). | (2.13) |
Therefore, one has
S[x(n),y(n)]−1=S′(x∗)−1(I−C2e(n)+(C22−C3)(e(n))2+(−2C23+2C2C3−C4)(e(n))3+O((e(n))4)). | (2.14) |
The error equations for the iterative method (1.6) give us
e(n+1)=C22(1+γ)(e(n))3+(−3C32(1+2γ)+C2C3(3+4γ))(e(n))4+O((e(n))5). | (2.15) |
The equation above shows that the iterative method converges at least to the third order. In particular, when the parameter γ=−1, the error equation is as follows:
e(n+1)=(3C32−C2C3)(e(n))4+O((e(n))5). | (2.16) |
When the parameter γ=1, the method (1.6) is a fourth-order convergent method with the following iteration format:
x(n+1)=x(n)+S′(x(n))−1(S(x(n))+S(y(n)))−2S[x(n),y(n)]−1S(x(n)), | (2.17) |
where
y(n)=x(n)−S′(x(n))−1S(x(n)). | (2.18) |
Fractal theory [25,26] can be used to study the stability and convergence of iterative methods. To visualize the convergence of the proposed iterative approach, fractal maps are constructed for a variety of nonlinear functions. Such methods have been employed in numerous research studies for practical applications. Lee et al.[27] studied a dynamic view of a class of single-parameter optimal eighth-order multi-root finders under Riemannian ball Möbius conjugate mappings using fractal knowledge. The complex dynamic behavior can be used for exploring the relevant properties of rational operators connected with iterative methods, which are the unique characteristics of rational operators closely connected with iterative methods, as demonstrated by the Möbius conjugate mappings on Riemann spheres. A thorough study of the behavior of the complex dynamics at fixed points allows us to perform a careful analysis of the stability of rational operators. A parameter space that was constructed by the critical point provides us with a reference to have a better idea of the stability in the iterative method, thus enabling us to pick more appropriate parameter values to make iterative methods more stable.
An in-depth study of the method's (1.6) complex dynamic behavior is presented in this section. First, we need to construct rational operators related to the iterative method (1.6).
Now, we will analyze the iterative method (1.5) in the context of quadratic polynomial dynamics. The rational operator can be constructed on an arbitrary nonlinear function by a principle based on Riemann ball dynamics [28] and the scalar theorem [29]. Therefore, a rational operator for quadratic polynomials is designed.
Theorem 2. Let E(x)=(x−m)(x−a) be some arbitrary polynomial of quadratic form, where m and a are its roots. The corresponding rational operator De(x;γ) of the family in (1.5) applied to E(x) is
De(x;γ)=x3(γ+(1+x)3)1+3x+3x2+(1+γ)x3. | (3.1) |
Proof. Applying the iterative method (1.5) in E(x), we obtain a rational function Be associated only with m, a, and γ. Next, we construct a rational function Be by applying the Möbius transform in Be with the
k(x)=x−mx−a. | (3.2) |
The properties of the conjugate mappings are as follows (see[30]):
k(∞)=1,k(a)=∞,k(m)=0. | (3.3) |
We can get
Be=x+(−1+γ)(m+a−2x)(m−x)(−a+x)m2+a2+m(a−3x)−3ax+3x2+γ(m−x)(a−x)(m2+3ma+a2−5mx−5ax+5x2)(m+a−2x)3. | (3.4) |
and
De(x;γ)=(k∘Be∘k−1)(x)=x3(γ+(1+x)3)1+3x+3x2+(1+γ)x3. | (3.5) |
According to the theorem above, we find that the expressive form of the rational operator De(x;γ) rests on the choice of the parameter value γ. Furthermore, it is known that Be(x) and De(x;γ) are conjugate to each other, and thus the expression for De(x;γ) contains only the parameter γ. The form of De(x,γ) is affected by the parameter γ, so it is only necessary to explore its relation to γ. When γ is −1 or 0, the expressions of De(x;γ) will be further simplified by factorizing De(x;γ) as follows:
De(x;−1)=x3((1+x)3−1)1+3x+3x2, | (3.6) |
and
De(x;0)=x3(1+x)31+3x+3x2+x3. | (3.7) |
In this section, the fixed points and the stability of the rational operator De(x;γ) are analyzed. The particular value of the parameter γ will determine the number of fixed points and their stability.
De(x)−x=x(x−1)λ(x)ϑ(x), | (3.8) |
where
λ(x)=1+4x−(−6+γ)x2+4x3+x4, | (3.9) |
and
ϑ(x)=1+3x+3x2+(1+γ)x3. | (3.10) |
From solving the equation De(x)−x=0, we know that the fixed points include x=0,x=∞,x=1, and the roots of the polynomial λ(x)=1+4x−(−6+γ)x2+4x3+x4.
Here, x=0 and x=∞ are the free points and fixed points of the parameter γ, respectively. Varying values of the parameter γ lead to different expressions for the iterative method (1.6). So, for the number of strange fixed points for different values of the parameter, we have Theorem 3.
Theorem 3. For λ(x) and ϑ(x), we have
● If γ=0, (1+x)3 is the common factor of the polynomial λ(x) and ϑ(x). In this case, the operator De(x) exhibits one unique strong fixed point, namely x=1.
● If γ=9, the operator De(x) has four strange fixed points, namely x=−4.79129,x=−0.208712, the complex number x=−0.5+0.866025i and x=0.5−0.866025i .
● If γ=1, in this case, the operator De(x) exhibits two strange fixed points namely x=−2.61803,x=−0.381966.
● If γ=16, in this case, the operator De(x) exhibits four strange fixed points namely x=−5.82843,x=1 (with multiplicity 2) and x=−0.171573.
● If γ satisfies (γ−1)(γ+1)(γ−(−1)13)(γ−(−1)23)≠0, the operator De(x) has nine strange fixed points: x=1 and the eight roots of the polynomial λ(x)=0. Then the operator De(x) will have five strange fixed points: in addition to x=1, there are four other roots of the polynomial λ(x)=0.
Proof. ● With λ(x)=0 and ϑ(x)=0, we can get
(1+x)(1+x+x2)=0. | (3.11) |
Suppose that x∈C is some root such that λ(x)=0 and ϑ(x)=0. Linking these two multinomial equations, we are in a position to remove the parameter γ, giving us the equation (1+x)(1+x+x2)=0. From (1+x)=0 or (1+x+x2)=0, we get x=−1 or (−1)13 or x=(−1)23. The common factorization of λ(x)=0 and ϑ(x)=0 is (1+x). As we substitute x=−1 for λ(x;−1) and ϑ(x;−1), we get the parameter γ=0. So for the operator De(x;0), there is now one anomaly in the fixed points.
● The remainder of the proof is consistent with the approach outlined earlier.
Theorem 3 allows us to conclude that the maximum number of fixed points is 5, while the minimum is 1. The stability of strange fixed points is discussed below. We first need to calculate the first-order derivatives of the iterative method's rational operator De(x;γ) with regard to the parameter γ. The first-order derivatives of De(x;γ) are computed as follows:
D′e(x;γ)=−3x2(1+x)2δ(x)ϱ(x)2, | (3.12) |
where
δ(x)=1+γ+4x+6x2+4x3+x4+γx4, | (3.13) |
ϱ(x)=1+3x+3x2+(1+γ)x3. | (3.14) |
The stability characteristics of the fixed points described above are discussed in detail next. With x=1, for the stability of any parameter value γ : ∀δ∈C∖{−8}, the following conclusions are obtained:
1) The point x is repulsive when |248+γ|>1;
2) The point x is attractive when |248+γ|<1;
3) The point x is parabolic when |248+γ|=1;
4) The point x is a superattracting point when |248+γ|≠0.
Proof. This follows from Eq (3.1):
D′e(x;γ)=−3x2(1+x)2δ(x)ϱ(x)2, | (3.15) |
where
δ(x)=1+γ+4x+6x2+4x3+x4+γx4, | (3.16) |
and
ϱ(x)=1+3x+3x2+(1+γ)x3. | (3.17) |
Substituting x=1 into (3.15), we acquire D′e(1;γ)=248+γ. It is easy to obtain |248+γ|=1⇔|24|=|8+γ|. Let γ=x+iy and (γ∈C). Then the following equation holds: (x+8)2+y2=242. Therefore, |D′e(1;γ)|>1⇔|γ+8|<24.
The strange fixed point is the root of x=1 and the polynomial λ(x)= 1+4x−(−6+γ)x2+4x3+x4. The roots of λ(x) are denoted as λi(x), for any i ranging from 1 to 4. For γ satisfying x(x−1)≠0, we have the following:
● If x=1, taking parameter values in the region [−35,20]×[−25,25], it is a point of attraction.
● λ1(x) is exclusive and independent of the value of the parameter γ.
● λ2(x), λ3(x), and λ4(x) act as attractors for the value of γ within the small region of the complex plane [−10,12.5]×[0.96,0.98], [−2.58,2.56]×[−10.96,12.98], [−1.58,1.56]×[−2.06,1.98].
Figure 1 displays the stability regions when x = 1. The repulsive region is depicted as a gray area, while the attractive region is shown as a gold area. When the selected parameter value is inside the disk, a behaves as a repelling point, while a becomes an attractor when the γ value is outside the disk. We are generally more attentive to values inside the disk. Figures 1 and 2 illustrate the stability characteristics of the strange fixed points for the operator De(x;γ).
On the basis of the definition of critical point, the critical point of the operator De(x;γ) is found through solving the equation D′e(x;γ)=0. Clearly, the critical points of the operator are 0 and 1, and these are closely related to the roots of the quadratic polynomial E(x)=(x−m)(x−a). Apart from this, the other critical points are considered to be defined as free critical points. The following theorem shows what happens when number of free critical points changes with various values of the parameter γ.
Theorem 4. If D′e(x;γ)=0, then x=−1 and δ(x)=0 correspond to a free critical point.
● The parameter γ takes the value of 0 when x=−1,δ(x)=(1+x)4, then the rational operator De(x;γ) has four free critical points, which are x=−1 (with multiplicity 4).
● The value of parameter γ is -8 when x=1, ς(x)=(1+x)4−8(1+x4), then the rational operator De(x;γ) has two free critical points, which are x=−0.714286−0.699854i and x=−0.714286+0.699854i.
● The value of the parameter γ is 1 when x=(−1)23, ς(x)=(1+x)4+(1+x4), at which point, the rational operator De(x;γ) has four free critical points, which are x=−1 (with multiplicity 2) and x=0 (with multiplicity 2).
● For any γ(γ+8)(γ−9)(γ−1) that is not equal to 0, we can learn that there are four free critical points.
cr1=−11+γ−τ−12√Ω−ξ−Δ−Γ;
cr2=−11+γ−τ+12√Ω−ξ−Δ−Γ;
cr3=−11+γ+τ−12√Ω−ξ−Δ+Γ;
cr4=−11+γ+τ+12√Ω−ξ−Δ+Γ;
where τ=√−γ+γ2√2√1+2γ+γ2;ξ=61+γ;Δ=2+2γ1+γ;Γ=√1+2γ+γ2(−64(1+γ)3+96(1+γ)2−321+γ)4√2√−γ+γ2;Ω=8(1+γ)2. In spite of the large number of free critical points, however, they are not completely independent of each other, but they are interconnected and together influence the dynamical behavior of the iterative method, as cr1=1cr2and cr3=1cr4.
Proof. Under the assumption that x∈C indicates certain values of δ(x)=0 if we ϱ(x)=0, and combine these two equations, by eliminating the parameter γ, we get (1+x)3(−1+x3)x3=0, which can be reduced to (1+x)3(−1+x3)=0. It follows that (1+x)3 and (−1+x3), which can be a common factorization of δ(x)=0 and ϱ(x)=0. As a result, substituting x=−1 for δ(x)=0 and ϱ(x)=0 gives the parameter γ=0, at which point δ(x)ϱ(x)=−2, and there are four critical points. The equation (−1+x3)=0 is solved to solve for x=1,x=−(−1)13 and x=(−1)23. The equation (−1+x3)=0 is then solved to solve for γ=1 and γ=−8. There are four critical points in D′e(x)=6x2(1+x)2(1+2x)2 when γ=1. When γ=−8, D′e(x)=−3x2(1+x)2(7+10x+7x2)(1+4x+7x2)2 has two critical points.
The dynamic plane H and the parameter plane Q are plotted and used to visualize the dynamical behavior associated with the iterative method (1.6). In the process of plotting the parameter plane, each γ-value is located in the same connected region of the parameter space, resulting in a series of subsets of methods (1.6) with similar dynamic behavior. Thus, more stable regions can be identified in the parameter plane, allowing us to extract more members with stability from the family of iterative methods (1.6).
Q={γ∈C : the free critical point cr whose orbit converges to κq∈˜C under the influence of the operator De(x;γ)}.
H={x∈C : for a given γ∈Q, the orbital of x(γ) tends towards a certain number κh∈˜C under the action of De(x;γ)}.
By Theorem 3, there are, at most, three free independent critical points that can be observed. Furthermore, we recognize that x=−1 is actually the mapping point of the fixed point x=1, and this critical point does not show a distinctive feature on the parameter plane. Thus, we can distinguish two unique parameter planes that each carry complementary information.
When analyzing the free critical point cr1 (or cr2) as the starting point for a series of iterative methods (1.6) associated with a particular complex value, we color the complex plane according to the convergence properties of the different methods. The corresponding point is labeled in red in the complex plane if the iterative method (1.6) converges to zero. If the convergence is to a non-zero value, then it will be marked in blue. For all other cases, points will be marked green. Figure 3 depicts the parameter plane for the iterative method with the initial point set to cr1. The figure is generated within the range of γ values [−40,20]×[−30,30] and [−5,5]×[−5,5], using a grid of 1000×1000 points and computed with 50 iterations for each point. Figure 3b provides a more detailed presentation of the situation in Figure 3(a). In Figures 3 and 4, it is observed that the red region represents the iterative method converging to 0, the blue region represents convergence to infinity, and the green region indicates convergence to 0. In this final parameter selection process, we should try to avoid using green parameter values and prioritize red or blue parameter values to ensure the stability of the iterative process.
The iterative method (1.6) has a dynamic plane influenced by the parameter γ, and the regions are marked with different colors. The region where the initial point converges to 0 we color orange, and the region that converges to infinity is marked blue. The region is green if the initial point converges to the fixed point x=1. Black regions indicate no convergence to any root if the initial point converges to black. Additionally, the iteration track is set to red. The dynamic planes are built on a 400×400 point grid, with each point undergoing an iterative process of up to 20 iterations. In Figure 5, the parameter planes that correspond to some specific parameter values are shown. When γ=0, the blue and orange regions are the only ones, indicating that the initial points converge only to 0 and infinity. This parameter value is therefore considered to be a desirable value to take. For γ=−8 and γ=1, some black regions appear in Figure 5, and the initial values of these black regions do not converge to any root, suggesting that these parameter values are not ideal choices.
Figure 6: Dynamic planes for special parameter values γ. We have selected the parameter values that satisfy the condition |8+γ|>24 and divided these values into two subsets; a group of smaller values, specifically γ=20,30, and a larger group of values including γ=1300,1340,−1500. A related graphical presentation can be found in Figure 6. We plot γ=−7000,10,000; refer to Figure 7.
The dynamic plane shows only orange and blue as stable parameter regions. The larger the absolute value of the parameter, the more complex the structure on the dynamic plane tends to be. Putting these observations into context, it is possible to conclude that the corresponding iterative method shows better stability when γ=0.
In this section, we conduct a series of numerical experiments aimed at verifying the theoretical convergence and stability predictions made in previous sections for the method (2.17)(XM). The iterative methods (2.17) (XM) are compared with Homeier's method (HM) [31] and Chun's method (CM)[32] for solving some nonlinear equations. Then two numerical experiments are carried out to solve the nonlinear systems.
Homeier's method (HM) [31]:
x(n+1)=x(n)−S(x(n))2(S′(x(n))−1+S′(y(n))−1), | (4.1) |
where y(n)=x(n)−S′(x(n))−1S(x(n)).
Chun's method [32]:
x(n+1)=x(n)−32S′(x(n))−1S(x(n))+12(S′(x(n))2)−1(S(x(n))S′(y(n))), | (4.2) |
where y(n)=x(n)−S′(x(n))−1S(x(n)).
Within Tables 1 and 2, the time column demonstrates the computing time, the x0 column labels the starting point in the iterative process, ACOC [33] denotes the approximate convergence order.
si(x) | Method | x0 | |x1−x0| | |x2−x1| | |x3−x2| | |x4−x3| | ACOC | Time |
s1(x) | XM | 1.4 | 3.476e-2 | 9.725e-6 | 2.2109e-16 | 2.5973e-48 | 2.9999996 | 0.390530 |
HM | 1.4 | 3.4769e-2 | 1.333e-6 | 7.8428e-20 | 1.4607e-59 | 3.000001 | 0.398433 | |
CM | 1.4 | 3.475e-2 | 2.0184e-5 | 4.2017e-15 | 3.7902e-44 | 2.9999984 | 0.409339 | |
XM | 1.3 | 6.5158e-2 | 2.0184e-5 | 4.2017e-15 | 3.7902e-44 | 2.9999984 | 0.409339 | |
HM | 1.3 | 6.5223e-2 | 6.91e-6 | 9.9906e-18 | 3.0194e-53 | 2.9999994 | 0.406029 | |
CM | 1.3 | 6.507e-2 | 1.6018e-4 | 2.1006e-12 | 4.7361e-36 | 3.000016 | 0.418919 | |
s2(z) | XM | 1.4 | 1.2322e-1 | 2.772e-3 | 2.3441e-8 | 1.4102e-23 | 3.0004456 | 0.378724 |
HM | 1.4 | 1.2576e-1 | 2.3771e-4 | 2.9752e-12 | 5.8369e-36 | 2.9999689 | 0.633551 | |
CM | 1.4 | 1.1826e-1 | 7.7388e-3 | 1.1455e-6 | 3.6246e-18 | 3.0030309 | 0.473145 | |
XM | 1.6 | 7.3619e-2 | 3.689e-4 | 6.3353e-11 | 2.7837e-31 | 2.9999538 | 0.364632 | |
HM | 1.6 | 7.3904e-2 | 1.0183e-4 | 2.341e-13 | 2.8435e-39 | 3.0000122 | 0.487294 | |
CM | 1.6 | 7.3235e-2 | 7.7125e-4 | 1.1033e-9 | 3.2379e-27 | 2.9998068 | 0.482667 | |
s3(z) | XM | 1.3 | 1.0363e-1 | 8.6367e-4 | 3.9673e-10 | 3.8196e-29 | 3.0001193 | 0.385904 |
HM | 1.3 | 1.0449e-1 | 6.1387e-6 | 1.0142e-17 | 4.5733e-53 | 2.9999987 | 0.889935 | |
CM | 1.3 | 1.0241e-1 | 2.0802e-3 | 1.1527e-8 | 1.9478e-24 | 3.0005625 | 0.780309 | |
XM | 1.5 | 9.5067e-2 | 4.414e-4 | 5.2757e-11 | 9.0156e-32 | 2.9999442 | 0.364020 | |
HM | 1.5 | 9.5456e-2 | 5.2515e-5 | 6.3513e-15 | 1.1233e-44 | 3.0000132 | 0.815432 | |
CM | 1.5 | 9.4686e-2 | 8.2275e-4 | 7.0641e-10 | 4.4833e-28 | 2.9998082 | 0.777321 | |
s4(z) | XM | 1.8 | 2.2924e-1 | 5.4852e-3 | 4.8052e-8 | 3.2011e-23 | 3.0007836 | 0.770896 |
HM | 1.8 | 2.3536e-1 | 6.3333e-4 | 1.0604e-12 | 5.0524e-39 | 2.9992499 | 0.919149 | |
CM | 1.8 | 2.2038e-1 | 1.4339e-2 | 1.7532e-6 | 3.087e-18 | 3.0041621 | 0.869809 | |
XM | 1.9 | 1.3384e-1 | 8.8166e-4 | 1.9802e-10 | 2.2404e-30 | 3.0000956 | 0.764242 | |
HM | 1.9 | 1.3478e-1 | 5.852e-5 | 8.4809e-16 | 2.5851e-48 | 2.9999442 | 0.899065 | |
CM | 1.9 | 13271e-1 | 2.0161e-3 | 4.7188e-9 | 6.0185e-26 | 3.0004016 | 0.958593 | |
s5(z) | XM | 0.7 | 1.0509e-1 | 1.0106e-4 | 7.3026e-14 | 2.755e-41 | 3.0000094 | 0.372409 |
HM | 0.7 | 1.0503e-1 | 1.5417e-4 | 4.4925e-13 | 1.1114e-38 | 3.0000055 | 0.387347 | |
CM | 0.7 | 1.0484e-1 | 3.4457e-4 | 1.0807e-11 | 3.3333e-34 | 3.0000218 | 0.711144 | |
XM | 0.9 | 3.0153e-1 | 3.6606e-3 | 3.4953e-9 | 3.0209e-27 | 3.0005195 | 0.363470 | |
HM | 0.9 | 3.0066e-1 | 4.5271e-3 | 1.1409e-8 | 1.8204e-25 | 3.0002463 | 0.411865 | |
CM | 0.9 | 2.9467e-1 | 1.0518e-2 | 3.1085e-7 | 7.9314e-21 | 3.0011099 | 0.726080 | |
s6(z) | XM | 0.2 | 9.8796e-2 | 2.0631e-4 | 1.9522e-12 | 1.6541e-36 | 2.999995 | 0.379134 |
HM | 0.2 | 9.8925e-2 | 7.689e-5 | 3.4635e-14 | 3.1654e-42 | 3.0000016 | 0.546499 | |
CM | 0.2 | 9.8549e-2 | 4.5356e-4 | 4.8574e-11 | 5.9694e-32 | 2.9999684 | 0.509358 | |
XM | 0.6 | 4.7715e-1 | 2.1853e-2 | 2.2984e-6 | 2.6993e-18 | 2.9989692 | 0.369832 | |
HM | 0.6 | 4.8842e-1 | 1.0583e-2 | 9.0743e-8 | 5.6928e-23 | 3.0004065 | 0.509111 | |
CM | 0.6 | 4.5978e-1 | 3.9191e-2 | 3.0088e-5 | 1.4186e-14 | 2.9942634 | 0.548055 |
si(x) | γ | x0 | |x1−x0| | |x2−x1| | |x3−x2| | |x4−x3| | ACOC | Time |
s1(x) | 0 | 0.9 | 4.2417e-1 | 4.1039e-2 | 1.7406e-5 | 1.2675e-15 | 3.0060079 | 0.395263 |
1 | 0.9 | 3.2788e-1 | 1.3573e-1 | 1.6133e-3 | 2.0244e-9 | 3.0657447 | 0.411651 | |
9 | 0.9 | 4.4244e-1 | 54.806 | 5.2807 | 4.7567 | nc | 0.463676 | |
s2(x) | 0 | 1.5 | 2.5974e-2 | 2.0187e-5 | 9.006e-15 | 7.9969e-43 | 3.0000018 | 0.365875 |
1 | 1.5 | 2.5952e-2 | 4.1889e-5 | 1.6095e-13 | 9.1294e-39 | 3.000007 | 0.406378 | |
9 | 1.5 | 2.5778e-2 | 2.1550e-4 | 1.0967e-10 | 1.4441e-29 | 3.0000642 | 0.524439 | |
s3(x) | 0 | 1.4 | 4.4916e-3 | 5.6144e-8 | 1.0866e-22 | 7.8771e-67 | 3.0 | 0.384539 |
1 | 1.4 | 4.4915-3 | 1.1286e-7 | 1.7652e-21 | 6.7536e-63 | 3.0 | 0.413546 | |
9 | 1.4 | 4.4911e-3 | 5.6657e-7 | 1.1166e-18 | 8.5487e-54 | 3.0000001 | 0.703668 | |
s4(x) | 0 | 2.0 | 3.4712e-2 | 1.2797e-5 | 6.0467e-16 | 6.3787e-47 | 3.0000009 | 0.796057 |
1 | 2.0 | 3.4699e-2 | 2.6357e-5 | 1.0566e-14 | 6.8067e-43 | 3.000003 | 0.813411 | |
9 | 2.0 | 3.459e-2 | 1.3483e-4 | 7.0755e-12 | 1.022e-33 | 3.0000251 | 0.801792 | |
s5(x) | 0 | 0.6 | 5.189e-3 | 9.9857e-9 | 7.044e-26 | 2.4725e-77 | 3.0 | 0.794878 |
1 | 0.6 | 5.189e-3 | 1.9977e-8 | 1.1281e-24 | 2.0311e-73 | 3.0 | 1.063982 | |
9 | 0.6 | 5.1889e-3 | 9.9912e-8 | 7.0557e-22 | 1.022e-33 | 3.0000251 | 0.801792 | |
s6(x) | 0 | 1.6 | 1.1355 | 3.5448e-1 | 9.0195e-3 | 1.6249e-7 | 2.975614 | 0.407156 |
1 | 1.6 | 1.0323 | 4.3713e-1 | 2.9580e-2 | 1.119e-5 | 2.925868 | 0.542887 | |
9 | 1.6 | 0.20652 | 2.785e-1 | 3.5908e-1 | 3.8939e-1 | nc | 0.554967 |
We use six nonlinear equations mentioned in references [34,35] for the in-depth study.
s1(x)=x3+4x2−10,ε≈1.3652300134140968.s2(x)=x4−log(x)−5,ε≈1.5259939537536892.s3(x)=sin2(x)−x2+1,ε≈1.4044916482153412.s4(x)=cos(πx2+x2−π),ε≈0.59480888843087377−0.0000852783334727i.s5(x)=arcsin(x2−1)−12x+1,ε≈0.59480888843087377. |
Additionally, we explore population growth patterns[36]: population dynamics are analyzed by means of a first-order linear ordinary differential equation as follows:
P′(x)=cP(x)+s, | (4.3) |
where c stands for a constant birth rate and s stands for a constant migration rate. P(x) is used to represent the total population size at any random time point x. By solving the linear differential equation, we can get its generalized solution
P(x)=P0ecx+sc(ecx−1), | (4.4) |
By P0, we denote the initial population size. Examining and exploring the multiple possible values of the parameters and combining them with the initial conditions provided in the literature [37], we can accurately calculate the birth rate by using the nonlinear equation below:
s6(x)=1564−1000ex−435x(ex−1),ε≈0.1009979328831604.
Here, c=k represents the ideal birth rate we wish to determine.
Example 1. Take the nonlinear system
{x1+ex1−cos(x2)=0,3x1−x2−sin(x2)=0, | (4.5) |
where x1 and x2 are the unknowns in the nonlinear system (4.5).
Example 2. Take the nonlinear the system of equations for the Hammerstein equation, which plays a crucial role in nonlinear integral equations:
s(x)=1+(15)∫10M(x,t)s(t)3dt, | (4.6) |
where x∈C[0,1],x,t∈[0,1]. The kernel function M is
M(x,t)={(1−x)tt≤x,x(1−t)x≤t. | (4.7) |
A system of nonlinear equations is formed by discretization and solved (4.6) by approximating the integral in Eq (4.6) by the Gauss-Legendre product method.
∫10x(t)dt≈7∑i=1wjx(tj), | (4.8) |
Here, tj and wj are the nodes in the Gauss-Legende product method and the weights. Denote the approximation of s(tj) as xi,i=1,…,7
si−1−157∑j=1aijs3j=0,i=1,…,7 | (4.9) |
where
ai,j={wjtj(1−ti)j≤i,wjti(1−tj)i<j. | (4.10) |
According to Table 1, the iterative method (1.6) has high computational accuracy. Compared with other methods, the iterative method (1.6) takes less time for calculation. In Table 2, different values of stabilization parameters such as γ=0,γ=1 and γ=9, are chosen, and the iterative method (1.6) has better convergence. Tables 3 and 4 are numerical experiments for solving nonlinear systems.
γ | iter | ‖x(k+1)−x(k)‖ | ‖S(x(k+1))‖ | ACOC |
-1 | 6 | 7.188e-505 | 1.457e-2017 | 4.00000 |
0 | 7 | 1.126e-440 | 5.584e-1321 | 3.00000 |
1 | 7 | 8.525e-362 | 4.847e-1084 | 3.00000 |
9 | 8 | 1.170e-450 | 6.260e-1350 | 3.00000 |
γ | iter | ‖x(k+1)−x(k)‖ | ‖S(x(k+1))‖ | ACOC |
-1 | 5 | 2.815e-312 | 8.479e-1250 | 4.00017 |
0 | 6 | 4.212e-367 | 2.286e-1102 | 3.00001 |
1 | 6 | 6.901e-327 | 2.011e-981 | 3.00001 |
9 | 6 | 1.800e-239 | 1.786e-718 | 3.00001 |
This section draws fractal graphs for different iterative methods used to solve the nonlinear equation S1(x)=x2−1. The iterative methods we compare include AM (when parameter γ=0, iterative method (1.6) is defined as AM), K2 (when parameter γ=−1, iterative method (1.6) is defined as K2), Solaiman's method (MH), and Kou's method (KW).
Solaiman's method [38] (MH):
x(n+1)=x(n)−S′(x(n))−1S(x(n))+2(4(S′(x(n)))4−4S(x(n))(S′(x(n)))2S″(x(n))+(S(x(n)))2(S″(x(n)))2)−1S(x(n))S′(x(n))S″(x(n)), | (5.1) |
Kou's method [39] (KW) :
x(n+1)=x(n)−S′(x(n))−1(S(y(n))−S(x(n))), | (5.2) |
where y(n)=x(n)+S′(x(n))−1S(x(n)).
We created a 500×500 point grid to generate images in the region D=[−5,5]×[−5,5]. The maximum number is 25 iterations. Blue, red, and yellow areas respectively, converge to the roots of the polynomials, whereas areas that do not converge within the maximum number of iterations are in black. The number of iterations is indicated by the brightness of the colors. For example, regions with fewer iterations show brighter colors. From the fractal maps, method AM showed better convergence than the other methods. Therefore, the iterative method (AM) has better convergence and stability than the other methods.
The paper investigates the complex dynamic behavior exhibited by Kou's extended iterative method, which is used to solve systems for nonlinear equations. Strange fixed points and critical points of the iterative method (1.6) are intensively studied. We found the relevant parameter plane and select some parameter values for further analysis. Some unique parameter planes with values such as γ=0,−1 and 9 are observed. By observing the parameter plane and the dynamic plane, we discover the parameter γ=0 to have good stability. Numerical experiments and dynamic analyses both agree with each other, verifying the accuracy of the conclusion. We will study the iterative methods with higher convergence and their stability in the future.
The authors declare they have not used artificial intelligence (AI) tools in the creation of this article.
This research was supported by Educational Commission Foundation of Liaoning Province of China (No. LJ212410167008), the Key Project of Bohai University (No. 0522xn078), and the "14th Five-Year Plan" Project of Liaoning Province Education Science (No. JG24EB014).
The authors declare there are no conflicts of interest.
[1] |
Akkemik KA (2012) Assessing the importance of international tourism for the Turkish economy: A social accounting matrix analysis. Tour Manag 33: 790–801. https://doi.org/10.1016/j.tourman.2011.09.002 doi: 10.1016/j.tourman.2011.09.002
![]() |
[2] | Bagnasco A (1977) Tre Italie. La problematica territoriale dello sviluppo italiano, Bologna. |
[3] | Banca d'Italia-Eurosistema (2022) Economie regionali. N. 9 - L'economia della Toscana. Rapporto annuale, giugno. Available from: https://www.bancaditalia.it/pubblicazioni/economie-regionali/2021/2021-0009/index.html. |
[4] | Becattini G (1979) Dal settore industriale al distretto industriale. Alcune considerazioni sull'unità di indagine dell'economia industriale, Rivista di economia e politica industriale, 55: 7–21. |
[5] | Becattini G (1989) Piccole e medie imprese e distretti industriali nel recente sviluppo italiano, Note economiche, 3: 397–412. |
[6] | Bellù LG (2012) Social Accounting Matrix (SAM) for analysing agricultural and rural development policies. Conceptual aspects and examples, 1–19. |
[7] | Bracalente B (2010) L'Umbria nell'Italia di mezzo verso il 2020: introduzione e sintesi della ricerca, In: Bracalente, B., Caratteri strutturali e scenari di sviluppo regionale. L'Umbria verso il 2020. Milano: Franco Angeli. |
[8] | Breisinger C, Thomas M, Thurlow J (2009) Social accounting matrices and multiplier analysis: An introduction with exercises. International food policy research institute. http://dx.doi.org/10.2499/9780896297838fsp5 |
[9] | CDP Think Tank. Focus Territori (2020) L'economia toscana: le 5 eccellenze da cui ripartire. Dicembre. Available from: https://www.cdp.it/sitointernet/page/it/leconomia_toscana_le_5_eccellenze_da_cui_ripartire?contentId = TNK31701. |
[10] | Civardi MB, Lenti RT (2008) Multiplier decomposition, inequality and poverty in a SAM framewor. |
[11] | De Anguita PM, Wagner JE (2010) Environmental social accounting matrices: theory and applications (1st ed.), Routledge. https://doi.org/10.4324/9780203854440 |
[12] | European Commission (2020) European Regional Competitiveness Index 2019. Available from: https://ec.europa.eu/regional_policy/sources/docgener/work/rci2019_scorecards.pdf. |
[13] | European Commission (2022) Digital Economy and Society Index (DESI). Available from: https://digital-strategy.ec.europa.eu/en/library/digital-economy-and-society-index-desi-2022. |
[14] | Eurostat (2011) Collaboration in Research and Methodology for Official Statistics. Handbook on Methodology of Modern Business Statistics. Available from: https://ec.europa.eu/eurostat/cros/content/ras-method_en. |
[15] | Eurostat (2022) Database. Available from: https://ec.europa.eu/eurostat/databrowser/view/tgs00047/default/table?lang = en. |
[16] | Ferrari G (1999) A la recherche de la matrice perdue, in Comptabilité Nationale. Nouvelles frontières, Edith Archambault et Michel Boë da (éds.). Economica, 297–310. |
[17] |
Ferrari G, Ma K, Mi Z (2020) SAM-based analysis of China's economic system and measurement of the effects of a VAT rate cut after the tax reform. Natl Account Rev 2: 26–52 https://doi.org/10.3934/NAR.2020002 doi: 10.3934/NAR.2020002
![]() |
[18] |
Ferrari G, Mondéjar Jiménez J, Secondi L (2018) Tourists' Expenditure in Tuscany and its impact on the regional economic system. J Clean Prod 171: 1437–1445. https://doi.org/10.1016/j.jclepro.2017.10.121 doi: 10.1016/j.jclepro.2017.10.121
![]() |
[19] |
Ferrari G, Mondéjar Jiménez J, Secondi L (2021) The role of tourism in China's economic system and growth. A Social Accounting Matrix (SAM)-based analysis. Econ Res 35: 252–272. https://doi.org/10.1080/1331677X.2021.1890178 doi: 10.1080/1331677X.2021.1890178
![]() |
[20] | Fofana I, Lemelin A, Cockburn J (2005) Balancing a Social Accounting Matrix: Theory and application. http://dx.doi.org/10.2139/ssrn.2439868 |
[21] | Hayes A (2023) The Supply Chain: from raw materials to order fulfilment. |
[22] | Holland DW, Wyeth P (1993) SAM multipliers: Their decomposition, interpretation and relationship to input-output multipliers, In: Otto, D.M., Microcomputer Based Input-output Modeling, Washington State University. https://doi.org/10.1201/9780429033797 |
[23] | Holst DR, Neal SH, Chaiwan A (2013) Social and Resource Accounting: Multiplier Analysis, UC Berkeley and Chiang Mai University Training Workshop, 14–18 January, Phnom Penh, Cambodia. |
[24] |
Keaney M (2021) One world, no longer: the past, the present, and the future of global value chains. Natl Account Rev 3: 1–49. https://doi.org/10.3934/NAR.2021001 doi: 10.3934/NAR.2021001
![]() |
[25] |
Keuning SJ (1991) Proposal for a Social Accounting Matrix which Fits into the Next System of National Accounts. Econ Syst Res 3: 233–248. https://doi.org/10.1080/09535319100000020 doi: 10.1080/09535319100000020
![]() |
[26] |
Keuning SJ, de Ruuter A (1988) Guidelines to the construction of a Social Accounting Matrix. Rev Income Wealth 34: 71–100. https://doi.org/10.1111/j.1475-4991.1988.tb00561.x doi: 10.1111/j.1475-4991.1988.tb00561.x
![]() |
[27] | IRPET (2020a) Economia. Available from: . |
[28] | IRPET (2020b) Il Sistema produttivo toscano. Firenze, maggio. Available from: http://www.irpet.it/wp-content/uploads/2020/09/irpet-il-sistema-produttivo-toscano.pdf. |
[29] | IRPET (2021) Note rapide. Firenze, ottobre. Available from: http://www.irpet.it/wp-content/uploads/2021/10/irpet-nota-rapida-n--7-ottobre-2021-rs.pdf. |
[30] | ISTAT (2011) Le tavole delle risorse e degli impieghi e la loro trasformazione in tavole simmetriche. Nota metodologica. Available from: https://www.istat.it/it/files//2011/01/nota_metodologica.pdf. |
[31] | ISTAT (2021) Conti economici territoriali. Anni 2018–2020. Available from: https://www.istat.it/it/archivio/265014#: ~: text = Nel%202020%2C%20il%20reddito%20disponibile%20delle%20famiglie%20%C3%A8%20diminuito%20del, Conti%20nazionali. |
[32] | ISTAT (2022a) National Accounts. Available from: http://dati.istat.it/?lang = en. |
[33] | ISTAT (2022b) Ricerca e sviluppo. Spesa-reg. Available from: http://dati.istat.it/index.aspx?queryid = 21942. |
[34] | Lima MC, Cardenete MA, Hewings GJ, et al. (2004) A structural analysis of a regional economy using Social Accounting Matrices: 1990–1999. Investig Reg-J Reg Res 5: 113–138. |
[35] |
Madsen B, Jensen-Butler C (2005) Spatial accounting methods and the construction of spatial social accounting matrices. Econ Syst Res 17: 187–210. https://doi.org/10.1080/09535310500114994 doi: 10.1080/09535310500114994
![]() |
[36] |
Madsen B, Zhang J (2010) Towards a new framework for accounting and modelling the regional and local impacts of tourism. Econ Syst Res 22: 313–340. https://doi.org/10.1080/09535314.2010.529067 doi: 10.1080/09535314.2010.529067
![]() |
[37] | Mainar-Causapé AJ, Ferrari E, McDonald S (2018) Social accounting matrices: basic aspects and main steps for estimation. Publications Office of the European Union: Luxembourg. Available from: file:///C:/Users/zhuan/Downloads/jrc_sams_manual-2018.pdf. |
[38] |
Morilla CR, Diaz-Salazar GL, Cardenete MA (2007) Economic and environmental efficiency using a social accounting matrix. Ecol Econ 60: 774–786. https://doi.org/10.1016/j.ecolecon.2006.02.012 doi: 10.1016/j.ecolecon.2006.02.012
![]() |
[39] |
Parikh A, Thorbecke E (1996) Impact of rural industrialization on village life and economy: a social accounting matrix approach. Econ Dev Cult Change 44: 351–377. https://doi.org/10.1086/452218 doi: 10.1086/452218
![]() |
[40] | Politecnico di Milano (2022) Misurare la digitalizzazione delle regioni italiane: il DESI regionale2021. Osservatorio agenda digitale. Webinar. Available from: https://www.pphc.it/desi-regioni-italia/. |
[41] | Polo C, Valle E (2009) Estimating tourism impacts using input-output and SAM models in the Balearic Islands, In: Matias, A., Nijkamp, P., Sarmento, M., (Eds.), Advances in Tourism Economics. New Developments, 121–143. |
[42] |
Pyatt G (1999) Some relationships between T-accounts, input–output tables and social accounting matrices. Econ Syst Res 11: 365–387. https://doi.org/10.1080/09535319900000027 doi: 10.1080/09535319900000027
![]() |
[43] |
Pyatt G (2001) Some Early Multiplier Models of the Relationship Between Income Distribution and Production Structure. Econ Syst Res 13: 139–164. https://doi.org/10.1080/09537320120052434 doi: 10.1080/09537320120052434
![]() |
[44] |
Pyatt G, Round JI (1977) Social Accounting Matrices for Development Planning. Rev Income Wealth 23: 339–364. https://doi.org/10.1111/j.1475-4991.1977.tb00022.x doi: 10.1111/j.1475-4991.1977.tb00022.x
![]() |
[45] | Pyatt G, Round JI (1979) Accounting and Fixed Price Multipliers in a SAM Framework. Econ J 89: 850–873. |
[46] | Pyatt G, Round JI (1985) Social Accounting Matrices: A Basis for Planning, The World Bank, Washington D C. |
[47] | Regione Toscana (2022a) Produzioni vegetali. Vitivinicolo. Available from: https://www.regione.toscana.it/vitivinicolo#: ~: text = Il%20settore%20vitivinicolo%20rappresenta%20per, circa%2060.000%20ettari%20di%20vigneti. |
[48] | Regione Toscana (2022b) Produzioni vegetali. Olio di oliva. Available from: https://www.regione.toscana.it/produzioni-vegetali/olio-di-oliva. |
[49] | Regione Toscana (2022c) Ambiente/Energia. La Geotermia. Available from: https://www.regione.toscana.it/-/geotermia. |
[50] | Regione Toscana (2019) Turismo termale e del benessere in Toscana. Available from: https://www.regione.toscana.it/-/turismo-termale-e-del-benessere-in-toscana. |
[51] | Regione Toscana (2022d) Il livello di digitalizzazione delle imprese toscane: dati 2021. Firenze. Available from: https://www.regione.toscana.it/-/il-livello-di-digitalizzazione-delle-imprese-toscane-dati-2021-benchmarking-nazionale-ed-europeo. |
[52] | Regione Toscana (2022e) Distretti EMAS della Toscana. Available from: https://www.regione.toscana.it/-/distretti-emas-della-toscana. |
[53] | Regione Toscana (2022f) Ambiente/Sviluppo sostenibile ed economia circolare. Available from: https://www.regione.toscana.it/-/distretto-conciario-s-croce-sull-arno. |
[54] | Regione Toscana (2022g) Ambiente/Sviluppo sostenibile ed economia circolare. Available from: https://www.regione.toscana.it/-/distretto-cartario-di-lucca. |
[55] |
Robinson S, Cattaneo A, and El-Said M (2001) Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods. Econ Syst Res 13: 47–64. https://doi.org/10.1080/09535310120026247 doi: 10.1080/09535310120026247
![]() |
[56] | Round J (2003) Social accounting matrices and SAM-based multiplier analysis, The impact of economic policies on poverty and income distribution: Evaluation techniques and tools, 14, 261–276. |
[57] | Santos S (2010) A quantitative approach to the effects of social policy measures. An application to Portugal, using Social Accounting Matrices, (No. 33/2010). EERI Research Paper Series. |
[58] | Santos S (2012) A SAM (Social Accounting Matrix) approach to the policy decision process, Instituto Superior de Economia e Gestã o - DE Working papers nº 28-2012/DE/UECE. |
[59] |
Scandizzo PL, Ferrarese C (2015) Social accounting matrix: A new estimation methodology. J Policy Model 37: 14–34. https://doi.org/10.1016/j.jpolmod.2015.01.007 doi: 10.1016/j.jpolmod.2015.01.007
![]() |
[60] | Stone R (1962) A Social Accounting Matrix for 1960, Chapman and Hall, Cambridge. |
[61] | Thorbecke E (2017) Social accounting matrices and social accounting analysis, In: Methods of interregional and regional analysis, 281–332, Routledge. |
[62] |
Van Wyk L, Saayman M, Rossouw R, et al. (2015). Regional economic impacts of events: A comparison of methods. South African J Econ Manag Sci 18: 155–176. https://doi.org/10.4102/sajems.v18i2.593 doi: 10.4102/sajems.v18i2.593
![]() |
[63] | Van Leeuwen ES, Nijkamp P, Rietveld P (2009) A Meta-analytic Comparison of Regional Output Multipliers at Different Spatial Levels: Economic Impacts of Tourism, In: Matias, Á., Nijkamp, P., Sarmento, M. (eds), Advances in Tourism Economics. New Developments, 13–33. https://doi.org/10.1007/978-3-7908-2124-6_2 |
[64] |
Xie J (2000) An environmentally extended social accounting matrix. Environ Resour Econ 16: 391–406. https://doi.org/10.1023/A:1008376618447 doi: 10.1023/A:1008376618447
![]() |
![]() |
![]() |
si(x) | Method | x0 | |x1−x0| | |x2−x1| | |x3−x2| | |x4−x3| | ACOC | Time |
s1(x) | XM | 1.4 | 3.476e-2 | 9.725e-6 | 2.2109e-16 | 2.5973e-48 | 2.9999996 | 0.390530 |
HM | 1.4 | 3.4769e-2 | 1.333e-6 | 7.8428e-20 | 1.4607e-59 | 3.000001 | 0.398433 | |
CM | 1.4 | 3.475e-2 | 2.0184e-5 | 4.2017e-15 | 3.7902e-44 | 2.9999984 | 0.409339 | |
XM | 1.3 | 6.5158e-2 | 2.0184e-5 | 4.2017e-15 | 3.7902e-44 | 2.9999984 | 0.409339 | |
HM | 1.3 | 6.5223e-2 | 6.91e-6 | 9.9906e-18 | 3.0194e-53 | 2.9999994 | 0.406029 | |
CM | 1.3 | 6.507e-2 | 1.6018e-4 | 2.1006e-12 | 4.7361e-36 | 3.000016 | 0.418919 | |
s2(z) | XM | 1.4 | 1.2322e-1 | 2.772e-3 | 2.3441e-8 | 1.4102e-23 | 3.0004456 | 0.378724 |
HM | 1.4 | 1.2576e-1 | 2.3771e-4 | 2.9752e-12 | 5.8369e-36 | 2.9999689 | 0.633551 | |
CM | 1.4 | 1.1826e-1 | 7.7388e-3 | 1.1455e-6 | 3.6246e-18 | 3.0030309 | 0.473145 | |
XM | 1.6 | 7.3619e-2 | 3.689e-4 | 6.3353e-11 | 2.7837e-31 | 2.9999538 | 0.364632 | |
HM | 1.6 | 7.3904e-2 | 1.0183e-4 | 2.341e-13 | 2.8435e-39 | 3.0000122 | 0.487294 | |
CM | 1.6 | 7.3235e-2 | 7.7125e-4 | 1.1033e-9 | 3.2379e-27 | 2.9998068 | 0.482667 | |
s3(z) | XM | 1.3 | 1.0363e-1 | 8.6367e-4 | 3.9673e-10 | 3.8196e-29 | 3.0001193 | 0.385904 |
HM | 1.3 | 1.0449e-1 | 6.1387e-6 | 1.0142e-17 | 4.5733e-53 | 2.9999987 | 0.889935 | |
CM | 1.3 | 1.0241e-1 | 2.0802e-3 | 1.1527e-8 | 1.9478e-24 | 3.0005625 | 0.780309 | |
XM | 1.5 | 9.5067e-2 | 4.414e-4 | 5.2757e-11 | 9.0156e-32 | 2.9999442 | 0.364020 | |
HM | 1.5 | 9.5456e-2 | 5.2515e-5 | 6.3513e-15 | 1.1233e-44 | 3.0000132 | 0.815432 | |
CM | 1.5 | 9.4686e-2 | 8.2275e-4 | 7.0641e-10 | 4.4833e-28 | 2.9998082 | 0.777321 | |
s4(z) | XM | 1.8 | 2.2924e-1 | 5.4852e-3 | 4.8052e-8 | 3.2011e-23 | 3.0007836 | 0.770896 |
HM | 1.8 | 2.3536e-1 | 6.3333e-4 | 1.0604e-12 | 5.0524e-39 | 2.9992499 | 0.919149 | |
CM | 1.8 | 2.2038e-1 | 1.4339e-2 | 1.7532e-6 | 3.087e-18 | 3.0041621 | 0.869809 | |
XM | 1.9 | 1.3384e-1 | 8.8166e-4 | 1.9802e-10 | 2.2404e-30 | 3.0000956 | 0.764242 | |
HM | 1.9 | 1.3478e-1 | 5.852e-5 | 8.4809e-16 | 2.5851e-48 | 2.9999442 | 0.899065 | |
CM | 1.9 | 13271e-1 | 2.0161e-3 | 4.7188e-9 | 6.0185e-26 | 3.0004016 | 0.958593 | |
s5(z) | XM | 0.7 | 1.0509e-1 | 1.0106e-4 | 7.3026e-14 | 2.755e-41 | 3.0000094 | 0.372409 |
HM | 0.7 | 1.0503e-1 | 1.5417e-4 | 4.4925e-13 | 1.1114e-38 | 3.0000055 | 0.387347 | |
CM | 0.7 | 1.0484e-1 | 3.4457e-4 | 1.0807e-11 | 3.3333e-34 | 3.0000218 | 0.711144 | |
XM | 0.9 | 3.0153e-1 | 3.6606e-3 | 3.4953e-9 | 3.0209e-27 | 3.0005195 | 0.363470 | |
HM | 0.9 | 3.0066e-1 | 4.5271e-3 | 1.1409e-8 | 1.8204e-25 | 3.0002463 | 0.411865 | |
CM | 0.9 | 2.9467e-1 | 1.0518e-2 | 3.1085e-7 | 7.9314e-21 | 3.0011099 | 0.726080 | |
s6(z) | XM | 0.2 | 9.8796e-2 | 2.0631e-4 | 1.9522e-12 | 1.6541e-36 | 2.999995 | 0.379134 |
HM | 0.2 | 9.8925e-2 | 7.689e-5 | 3.4635e-14 | 3.1654e-42 | 3.0000016 | 0.546499 | |
CM | 0.2 | 9.8549e-2 | 4.5356e-4 | 4.8574e-11 | 5.9694e-32 | 2.9999684 | 0.509358 | |
XM | 0.6 | 4.7715e-1 | 2.1853e-2 | 2.2984e-6 | 2.6993e-18 | 2.9989692 | 0.369832 | |
HM | 0.6 | 4.8842e-1 | 1.0583e-2 | 9.0743e-8 | 5.6928e-23 | 3.0004065 | 0.509111 | |
CM | 0.6 | 4.5978e-1 | 3.9191e-2 | 3.0088e-5 | 1.4186e-14 | 2.9942634 | 0.548055 |
si(x) | γ | x0 | |x1−x0| | |x2−x1| | |x3−x2| | |x4−x3| | ACOC | Time |
s1(x) | 0 | 0.9 | 4.2417e-1 | 4.1039e-2 | 1.7406e-5 | 1.2675e-15 | 3.0060079 | 0.395263 |
1 | 0.9 | 3.2788e-1 | 1.3573e-1 | 1.6133e-3 | 2.0244e-9 | 3.0657447 | 0.411651 | |
9 | 0.9 | 4.4244e-1 | 54.806 | 5.2807 | 4.7567 | nc | 0.463676 | |
s2(x) | 0 | 1.5 | 2.5974e-2 | 2.0187e-5 | 9.006e-15 | 7.9969e-43 | 3.0000018 | 0.365875 |
1 | 1.5 | 2.5952e-2 | 4.1889e-5 | 1.6095e-13 | 9.1294e-39 | 3.000007 | 0.406378 | |
9 | 1.5 | 2.5778e-2 | 2.1550e-4 | 1.0967e-10 | 1.4441e-29 | 3.0000642 | 0.524439 | |
s3(x) | 0 | 1.4 | 4.4916e-3 | 5.6144e-8 | 1.0866e-22 | 7.8771e-67 | 3.0 | 0.384539 |
1 | 1.4 | 4.4915-3 | 1.1286e-7 | 1.7652e-21 | 6.7536e-63 | 3.0 | 0.413546 | |
9 | 1.4 | 4.4911e-3 | 5.6657e-7 | 1.1166e-18 | 8.5487e-54 | 3.0000001 | 0.703668 | |
s4(x) | 0 | 2.0 | 3.4712e-2 | 1.2797e-5 | 6.0467e-16 | 6.3787e-47 | 3.0000009 | 0.796057 |
1 | 2.0 | 3.4699e-2 | 2.6357e-5 | 1.0566e-14 | 6.8067e-43 | 3.000003 | 0.813411 | |
9 | 2.0 | 3.459e-2 | 1.3483e-4 | 7.0755e-12 | 1.022e-33 | 3.0000251 | 0.801792 | |
s5(x) | 0 | 0.6 | 5.189e-3 | 9.9857e-9 | 7.044e-26 | 2.4725e-77 | 3.0 | 0.794878 |
1 | 0.6 | 5.189e-3 | 1.9977e-8 | 1.1281e-24 | 2.0311e-73 | 3.0 | 1.063982 | |
9 | 0.6 | 5.1889e-3 | 9.9912e-8 | 7.0557e-22 | 1.022e-33 | 3.0000251 | 0.801792 | |
s6(x) | 0 | 1.6 | 1.1355 | 3.5448e-1 | 9.0195e-3 | 1.6249e-7 | 2.975614 | 0.407156 |
1 | 1.6 | 1.0323 | 4.3713e-1 | 2.9580e-2 | 1.119e-5 | 2.925868 | 0.542887 | |
9 | 1.6 | 0.20652 | 2.785e-1 | 3.5908e-1 | 3.8939e-1 | nc | 0.554967 |
γ | iter | ‖x(k+1)−x(k)‖ | ‖S(x(k+1))‖ | ACOC |
-1 | 6 | 7.188e-505 | 1.457e-2017 | 4.00000 |
0 | 7 | 1.126e-440 | 5.584e-1321 | 3.00000 |
1 | 7 | 8.525e-362 | 4.847e-1084 | 3.00000 |
9 | 8 | 1.170e-450 | 6.260e-1350 | 3.00000 |
γ | iter | ‖x(k+1)−x(k)‖ | ‖S(x(k+1))‖ | ACOC |
-1 | 5 | 2.815e-312 | 8.479e-1250 | 4.00017 |
0 | 6 | 4.212e-367 | 2.286e-1102 | 3.00001 |
1 | 6 | 6.901e-327 | 2.011e-981 | 3.00001 |
9 | 6 | 1.800e-239 | 1.786e-718 | 3.00001 |
si(x) | Method | x0 | |x1−x0| | |x2−x1| | |x3−x2| | |x4−x3| | ACOC | Time |
s1(x) | XM | 1.4 | 3.476e-2 | 9.725e-6 | 2.2109e-16 | 2.5973e-48 | 2.9999996 | 0.390530 |
HM | 1.4 | 3.4769e-2 | 1.333e-6 | 7.8428e-20 | 1.4607e-59 | 3.000001 | 0.398433 | |
CM | 1.4 | 3.475e-2 | 2.0184e-5 | 4.2017e-15 | 3.7902e-44 | 2.9999984 | 0.409339 | |
XM | 1.3 | 6.5158e-2 | 2.0184e-5 | 4.2017e-15 | 3.7902e-44 | 2.9999984 | 0.409339 | |
HM | 1.3 | 6.5223e-2 | 6.91e-6 | 9.9906e-18 | 3.0194e-53 | 2.9999994 | 0.406029 | |
CM | 1.3 | 6.507e-2 | 1.6018e-4 | 2.1006e-12 | 4.7361e-36 | 3.000016 | 0.418919 | |
s2(z) | XM | 1.4 | 1.2322e-1 | 2.772e-3 | 2.3441e-8 | 1.4102e-23 | 3.0004456 | 0.378724 |
HM | 1.4 | 1.2576e-1 | 2.3771e-4 | 2.9752e-12 | 5.8369e-36 | 2.9999689 | 0.633551 | |
CM | 1.4 | 1.1826e-1 | 7.7388e-3 | 1.1455e-6 | 3.6246e-18 | 3.0030309 | 0.473145 | |
XM | 1.6 | 7.3619e-2 | 3.689e-4 | 6.3353e-11 | 2.7837e-31 | 2.9999538 | 0.364632 | |
HM | 1.6 | 7.3904e-2 | 1.0183e-4 | 2.341e-13 | 2.8435e-39 | 3.0000122 | 0.487294 | |
CM | 1.6 | 7.3235e-2 | 7.7125e-4 | 1.1033e-9 | 3.2379e-27 | 2.9998068 | 0.482667 | |
s3(z) | XM | 1.3 | 1.0363e-1 | 8.6367e-4 | 3.9673e-10 | 3.8196e-29 | 3.0001193 | 0.385904 |
HM | 1.3 | 1.0449e-1 | 6.1387e-6 | 1.0142e-17 | 4.5733e-53 | 2.9999987 | 0.889935 | |
CM | 1.3 | 1.0241e-1 | 2.0802e-3 | 1.1527e-8 | 1.9478e-24 | 3.0005625 | 0.780309 | |
XM | 1.5 | 9.5067e-2 | 4.414e-4 | 5.2757e-11 | 9.0156e-32 | 2.9999442 | 0.364020 | |
HM | 1.5 | 9.5456e-2 | 5.2515e-5 | 6.3513e-15 | 1.1233e-44 | 3.0000132 | 0.815432 | |
CM | 1.5 | 9.4686e-2 | 8.2275e-4 | 7.0641e-10 | 4.4833e-28 | 2.9998082 | 0.777321 | |
s4(z) | XM | 1.8 | 2.2924e-1 | 5.4852e-3 | 4.8052e-8 | 3.2011e-23 | 3.0007836 | 0.770896 |
HM | 1.8 | 2.3536e-1 | 6.3333e-4 | 1.0604e-12 | 5.0524e-39 | 2.9992499 | 0.919149 | |
CM | 1.8 | 2.2038e-1 | 1.4339e-2 | 1.7532e-6 | 3.087e-18 | 3.0041621 | 0.869809 | |
XM | 1.9 | 1.3384e-1 | 8.8166e-4 | 1.9802e-10 | 2.2404e-30 | 3.0000956 | 0.764242 | |
HM | 1.9 | 1.3478e-1 | 5.852e-5 | 8.4809e-16 | 2.5851e-48 | 2.9999442 | 0.899065 | |
CM | 1.9 | 13271e-1 | 2.0161e-3 | 4.7188e-9 | 6.0185e-26 | 3.0004016 | 0.958593 | |
s5(z) | XM | 0.7 | 1.0509e-1 | 1.0106e-4 | 7.3026e-14 | 2.755e-41 | 3.0000094 | 0.372409 |
HM | 0.7 | 1.0503e-1 | 1.5417e-4 | 4.4925e-13 | 1.1114e-38 | 3.0000055 | 0.387347 | |
CM | 0.7 | 1.0484e-1 | 3.4457e-4 | 1.0807e-11 | 3.3333e-34 | 3.0000218 | 0.711144 | |
XM | 0.9 | 3.0153e-1 | 3.6606e-3 | 3.4953e-9 | 3.0209e-27 | 3.0005195 | 0.363470 | |
HM | 0.9 | 3.0066e-1 | 4.5271e-3 | 1.1409e-8 | 1.8204e-25 | 3.0002463 | 0.411865 | |
CM | 0.9 | 2.9467e-1 | 1.0518e-2 | 3.1085e-7 | 7.9314e-21 | 3.0011099 | 0.726080 | |
s6(z) | XM | 0.2 | 9.8796e-2 | 2.0631e-4 | 1.9522e-12 | 1.6541e-36 | 2.999995 | 0.379134 |
HM | 0.2 | 9.8925e-2 | 7.689e-5 | 3.4635e-14 | 3.1654e-42 | 3.0000016 | 0.546499 | |
CM | 0.2 | 9.8549e-2 | 4.5356e-4 | 4.8574e-11 | 5.9694e-32 | 2.9999684 | 0.509358 | |
XM | 0.6 | 4.7715e-1 | 2.1853e-2 | 2.2984e-6 | 2.6993e-18 | 2.9989692 | 0.369832 | |
HM | 0.6 | 4.8842e-1 | 1.0583e-2 | 9.0743e-8 | 5.6928e-23 | 3.0004065 | 0.509111 | |
CM | 0.6 | 4.5978e-1 | 3.9191e-2 | 3.0088e-5 | 1.4186e-14 | 2.9942634 | 0.548055 |
si(x) | γ | x0 | |x1−x0| | |x2−x1| | |x3−x2| | |x4−x3| | ACOC | Time |
s1(x) | 0 | 0.9 | 4.2417e-1 | 4.1039e-2 | 1.7406e-5 | 1.2675e-15 | 3.0060079 | 0.395263 |
1 | 0.9 | 3.2788e-1 | 1.3573e-1 | 1.6133e-3 | 2.0244e-9 | 3.0657447 | 0.411651 | |
9 | 0.9 | 4.4244e-1 | 54.806 | 5.2807 | 4.7567 | nc | 0.463676 | |
s2(x) | 0 | 1.5 | 2.5974e-2 | 2.0187e-5 | 9.006e-15 | 7.9969e-43 | 3.0000018 | 0.365875 |
1 | 1.5 | 2.5952e-2 | 4.1889e-5 | 1.6095e-13 | 9.1294e-39 | 3.000007 | 0.406378 | |
9 | 1.5 | 2.5778e-2 | 2.1550e-4 | 1.0967e-10 | 1.4441e-29 | 3.0000642 | 0.524439 | |
s3(x) | 0 | 1.4 | 4.4916e-3 | 5.6144e-8 | 1.0866e-22 | 7.8771e-67 | 3.0 | 0.384539 |
1 | 1.4 | 4.4915-3 | 1.1286e-7 | 1.7652e-21 | 6.7536e-63 | 3.0 | 0.413546 | |
9 | 1.4 | 4.4911e-3 | 5.6657e-7 | 1.1166e-18 | 8.5487e-54 | 3.0000001 | 0.703668 | |
s4(x) | 0 | 2.0 | 3.4712e-2 | 1.2797e-5 | 6.0467e-16 | 6.3787e-47 | 3.0000009 | 0.796057 |
1 | 2.0 | 3.4699e-2 | 2.6357e-5 | 1.0566e-14 | 6.8067e-43 | 3.000003 | 0.813411 | |
9 | 2.0 | 3.459e-2 | 1.3483e-4 | 7.0755e-12 | 1.022e-33 | 3.0000251 | 0.801792 | |
s5(x) | 0 | 0.6 | 5.189e-3 | 9.9857e-9 | 7.044e-26 | 2.4725e-77 | 3.0 | 0.794878 |
1 | 0.6 | 5.189e-3 | 1.9977e-8 | 1.1281e-24 | 2.0311e-73 | 3.0 | 1.063982 | |
9 | 0.6 | 5.1889e-3 | 9.9912e-8 | 7.0557e-22 | 1.022e-33 | 3.0000251 | 0.801792 | |
s6(x) | 0 | 1.6 | 1.1355 | 3.5448e-1 | 9.0195e-3 | 1.6249e-7 | 2.975614 | 0.407156 |
1 | 1.6 | 1.0323 | 4.3713e-1 | 2.9580e-2 | 1.119e-5 | 2.925868 | 0.542887 | |
9 | 1.6 | 0.20652 | 2.785e-1 | 3.5908e-1 | 3.8939e-1 | nc | 0.554967 |
γ | iter | ‖x(k+1)−x(k)‖ | ‖S(x(k+1))‖ | ACOC |
-1 | 6 | 7.188e-505 | 1.457e-2017 | 4.00000 |
0 | 7 | 1.126e-440 | 5.584e-1321 | 3.00000 |
1 | 7 | 8.525e-362 | 4.847e-1084 | 3.00000 |
9 | 8 | 1.170e-450 | 6.260e-1350 | 3.00000 |
γ | iter | ‖x(k+1)−x(k)‖ | ‖S(x(k+1))‖ | ACOC |
-1 | 5 | 2.815e-312 | 8.479e-1250 | 4.00017 |
0 | 6 | 4.212e-367 | 2.286e-1102 | 3.00001 |
1 | 6 | 6.901e-327 | 2.011e-981 | 3.00001 |
9 | 6 | 1.800e-239 | 1.786e-718 | 3.00001 |