doi: 10.3934/jcd.2021002

A self-consistent dynamical system with multiple absolutely continuous invariant measures

Laboratoire de Probabilités, Statistique et Modélisation (LPSM), Sorbonne Université, Université de Paris, 4 Place Jussieu, 75005 Paris, France

 

Received  September 2019 Revised  June 2020 Published  August 2020

Fund Project: *The author was supported by ERC grant No 787304 and NKFIH OTKA grant K123782

In this paper we study a class of self-consistent dynamical systems, self-consistent in the sense that the discrete time dynamics is different in each step depending on current statistics. The general framework admits popular examples such as coupled map systems. Motivated by an example of [9], we concentrate on a special case where the dynamics in each step is a $ \beta $-map with some $ \beta \geq 2 $. Included in the definition of $ \beta $ is a parameter $ \varepsilon > 0 $ controlling the strength of self-consistency. We show such a self-consistent system which has a unique absolutely continuous invariant measure (acim) for $ \varepsilon = 0 $, but at least two for any $ \varepsilon > 0 $. With a slight modification, we transform this system into one which produces a phase transition-like behavior: it has a unique acim for $ 0< \varepsilon < \varepsilon^* $, and multiple for sufficiently large values of $ \varepsilon $. We discuss the stability of the invariant measures by the help of computer simulations employing the numerical representation of the self-consistent transfer operator.

Citation: Fanni M. Sélley. A self-consistent dynamical system with multiple absolutely continuous invariant measures. Journal of Computational Dynamics, doi: 10.3934/jcd.2021002
References:
[1]

L. Arnold, Random Dynamical Systems, Springer Monographs in Mathematics, Springer Verlag, Berlin, 1998. doi: 10.1007/978-3-662-12878-7.  Google Scholar

[2]

V. Baladi and L-S. Young, On the spectra of randomly perturbed expanding maps, Commun. Math. Phys., 2 (1993), 355-385.  doi: 10.1007/BF02098487.  Google Scholar

[3]

V. BaladiA. Kondah and B. Schmitt, Random correlations for small perturbations of expanding maps, Random Comput. Dyn., 2-3 (1996), 179-204.   Google Scholar

[4]

P. Bálint, G. Keller, F. Sélley and I. P. Tóth, Synchronization versus stability of the invariant distribution for a class of globally coupled maps, Nonlinearity, 8 (2018), 3770. doi: 10.1088/1361-6544/aac5b0.  Google Scholar

[5]

J.-B. BardetG. Keller and R. Zweimüller, Stochastically stable globally coupled maps with bistable thermodynamic limit, Commun. Math. Phys., 1 (2009), 237-270.  doi: 10.1007/s00220-009-0854-9.  Google Scholar

[6]

M. Blank and G. Keller, Random perturbations of chaotic dynamical systems: Stability of the spectrum, Nonlinearity, 5 (1998), 1351. doi: 10.1088/0951-7715/11/5/010.  Google Scholar

[7]

M. Blank, Collective phenomena in lattices of weakly interacting maps, Dokl. Akad. Nauk., 3 (2010), 300-304.  doi: 10.1134/S1064562410010126.  Google Scholar

[8]

M. Blank, Self-consistent mappings and systems of interacting particles, Dokl. Math., 1 (2011), 49-52.  doi: 10.1134/S1064562411010133.  Google Scholar

[9]

M. Blank, Ergodic averaging with and without invariant measures, Nonlinearity, 8 (2017), 4649. doi: 10.1088/1361-6544/aa8fe8.  Google Scholar

[10]

T. Bogenschütz, Stochastic stability of invariant subspaces, Ergod. Theor. Dyn. Syst., 3 (2000), 663-680.  doi: 10.1017/S0143385700000353.  Google Scholar

[11]

J. Buzzi, Absolutely continuous SRB measures for random Lasota–Yorke maps, T. Am. Math. Soc., 7 (2000), 3289-3303.  doi: 10.1090/S0002-9947-00-02607-6.  Google Scholar

[12]

A. BoyarskyP. Góra and C. Keefe, Absolutely continuous invariant measures for non-autonomous dynamical systems, J. Math. Anal. Appl., 1 (2019), 159-168.  doi: 10.1016/j.jmaa.2018.09.060.  Google Scholar

[13]

J-R. Chazottes and B. Fernandez, Dynamics of Coupled Map Lattices and of Related Spatially Extended Systems, Springer Science & Business Media, 2005.  Google Scholar

[14]

J. DingQ. Du and T-Y. Li, High order approximation of the Frobenius–Perron operator, Appl. Math. Comput., 2-3 (1993), 151-171.  doi: 10.1016/0096-3003(93)90099-Z.  Google Scholar

[15]

J. Ding and A. Zhou, Finite approximations of Frobenius–Perron operators. A solution of {U}lam's conjecture to multi-dimensional transformations, Physica D, 1-2 (1996), 61-68.  doi: 10.1016/0167-2789(95)00292-8.  Google Scholar

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G. Froyland, Finite approximation of Sinai-Bowen-Ruelle measures for Anosov systems in two dimensions, Random Comput. Dyn., 4 (1995), 251-264.   Google Scholar

[17]

G. Froyland, Ulam's method for random interval maps, Nonlinearity, 4 (1999), 1029. doi: 10.1088/0951-7715/12/4/318.  Google Scholar

[18]

G. Froyland, C. González-Tokman and A. Quas, Stability and approximation of random invariant densities for Lasota–Yorke map cocycles, Nonlinearity, 4 (2014), 647. doi: 10.1088/0951-7715/27/4/647.  Google Scholar

[19]

G. FroylandC. González-Tokman and R. D. A. Murray, Quenched stochastic stability for eventually expanding-on-average random interval map cocycles, Ergod. Theor. Dyn. Syst., 10 (2019), 2769-2792.  doi: 10.1017/etds.2017.143.  Google Scholar

[20]

M. Jiang and Y. B. Pesin, Equilibrium measures for coupled map lattices: Existence, uniqueness and finite-dimensional approximations, Commun. Math. Phys., 3 (1998), 675-711.  doi: 10.1007/s002200050344.  Google Scholar

[21]

K. Kaneko, Globally coupled chaos violates the law of large numbers but not the central limit theorem, Phys. Rev. Lett., 12 (1990), 1391. doi: 10.1103/PhysRevLett.65.1391.  Google Scholar

[22]

C. Liverani and G. Keller, A spectral gap for a one-dimensional lattice of coupled piecewise expanding interval maps, in Dynamics of Coupled Map Lattices and of Related Spatially Extended Systems, Springer, 2005,115–151. doi: 10.1007/11360810_6.  Google Scholar

[23]

G. Keller, R. Klages and P. J. Howard, Continuity properties of transport coefficients in simple maps, Nonlinearity, 8 (2008), 1719. doi: 10.1088/0951-7715/21/8/003.  Google Scholar

[24]

G. Keller, An ergodic theoretic approach to mean field coupled maps, Prog. Probab., (2000), 183–208.  Google Scholar

[25]

S. Klus, P. Koltai and C. Schütte, On the numerical approximation of the Perron–Frobenius and Koopman operator, J. Comput. Dyn., 1 (2016), 51. doi: 10.3934/jcd.2016003.  Google Scholar

[26]

T-Y. Li, Finite approximation for the Frobenius–Perron operator. A solution to Ulam's conjecture, J. Approx Theory, 2 (1976), 177-186.  doi: 10.1016/0021-9045(76)90037-X.  Google Scholar

[27]

R. D. A. Murray, Discrete Approximation of Invariant Densities, University of Cambridge, 1997. Google Scholar

[28]

W. Parry, On the $\beta$-expansions of real numbers, Acta Math. Hung., 3-4 (1960), 401-416.  doi: 10.1007/BF02020954.  Google Scholar

[29]

W. Parry, Representations for real numbers, Acta Math. Hung., 1-2 (1964), 95-105.  doi: 10.1007/BF01897025.  Google Scholar

[30]

A. Rényi, Representations for real numbers and their ergodic properties, Acta Math. Hung., 3-4 (1957), 477-493.  doi: 10.1007/BF02020331.  Google Scholar

[31]

F. M. Sélley, Asymptotic Properties of Mean Field Coupled Maps, Ph.D thesis, Budapest University of Technology and Economics, 2019. Google Scholar

[32]

W. OttM. Stenlund and L-S. Young, Memory loss for time-dependent dynamical systems, Math. Res. Lett., 2 (2009), 463-475.  doi: 10.4310/MRL.2009.v16.n3.a7.  Google Scholar

[33]

S. M. Ulam, A Collection of Mathematical Problems, Interscience Tracts in Pure and Applied Mathematics, New York, 1960.  Google Scholar

show all references

References:
[1]

L. Arnold, Random Dynamical Systems, Springer Monographs in Mathematics, Springer Verlag, Berlin, 1998. doi: 10.1007/978-3-662-12878-7.  Google Scholar

[2]

V. Baladi and L-S. Young, On the spectra of randomly perturbed expanding maps, Commun. Math. Phys., 2 (1993), 355-385.  doi: 10.1007/BF02098487.  Google Scholar

[3]

V. BaladiA. Kondah and B. Schmitt, Random correlations for small perturbations of expanding maps, Random Comput. Dyn., 2-3 (1996), 179-204.   Google Scholar

[4]

P. Bálint, G. Keller, F. Sélley and I. P. Tóth, Synchronization versus stability of the invariant distribution for a class of globally coupled maps, Nonlinearity, 8 (2018), 3770. doi: 10.1088/1361-6544/aac5b0.  Google Scholar

[5]

J.-B. BardetG. Keller and R. Zweimüller, Stochastically stable globally coupled maps with bistable thermodynamic limit, Commun. Math. Phys., 1 (2009), 237-270.  doi: 10.1007/s00220-009-0854-9.  Google Scholar

[6]

M. Blank and G. Keller, Random perturbations of chaotic dynamical systems: Stability of the spectrum, Nonlinearity, 5 (1998), 1351. doi: 10.1088/0951-7715/11/5/010.  Google Scholar

[7]

M. Blank, Collective phenomena in lattices of weakly interacting maps, Dokl. Akad. Nauk., 3 (2010), 300-304.  doi: 10.1134/S1064562410010126.  Google Scholar

[8]

M. Blank, Self-consistent mappings and systems of interacting particles, Dokl. Math., 1 (2011), 49-52.  doi: 10.1134/S1064562411010133.  Google Scholar

[9]

M. Blank, Ergodic averaging with and without invariant measures, Nonlinearity, 8 (2017), 4649. doi: 10.1088/1361-6544/aa8fe8.  Google Scholar

[10]

T. Bogenschütz, Stochastic stability of invariant subspaces, Ergod. Theor. Dyn. Syst., 3 (2000), 663-680.  doi: 10.1017/S0143385700000353.  Google Scholar

[11]

J. Buzzi, Absolutely continuous SRB measures for random Lasota–Yorke maps, T. Am. Math. Soc., 7 (2000), 3289-3303.  doi: 10.1090/S0002-9947-00-02607-6.  Google Scholar

[12]

A. BoyarskyP. Góra and C. Keefe, Absolutely continuous invariant measures for non-autonomous dynamical systems, J. Math. Anal. Appl., 1 (2019), 159-168.  doi: 10.1016/j.jmaa.2018.09.060.  Google Scholar

[13]

J-R. Chazottes and B. Fernandez, Dynamics of Coupled Map Lattices and of Related Spatially Extended Systems, Springer Science & Business Media, 2005.  Google Scholar

[14]

J. DingQ. Du and T-Y. Li, High order approximation of the Frobenius–Perron operator, Appl. Math. Comput., 2-3 (1993), 151-171.  doi: 10.1016/0096-3003(93)90099-Z.  Google Scholar

[15]

J. Ding and A. Zhou, Finite approximations of Frobenius–Perron operators. A solution of {U}lam's conjecture to multi-dimensional transformations, Physica D, 1-2 (1996), 61-68.  doi: 10.1016/0167-2789(95)00292-8.  Google Scholar

[16]

G. Froyland, Finite approximation of Sinai-Bowen-Ruelle measures for Anosov systems in two dimensions, Random Comput. Dyn., 4 (1995), 251-264.   Google Scholar

[17]

G. Froyland, Ulam's method for random interval maps, Nonlinearity, 4 (1999), 1029. doi: 10.1088/0951-7715/12/4/318.  Google Scholar

[18]

G. Froyland, C. González-Tokman and A. Quas, Stability and approximation of random invariant densities for Lasota–Yorke map cocycles, Nonlinearity, 4 (2014), 647. doi: 10.1088/0951-7715/27/4/647.  Google Scholar

[19]

G. FroylandC. González-Tokman and R. D. A. Murray, Quenched stochastic stability for eventually expanding-on-average random interval map cocycles, Ergod. Theor. Dyn. Syst., 10 (2019), 2769-2792.  doi: 10.1017/etds.2017.143.  Google Scholar

[20]

M. Jiang and Y. B. Pesin, Equilibrium measures for coupled map lattices: Existence, uniqueness and finite-dimensional approximations, Commun. Math. Phys., 3 (1998), 675-711.  doi: 10.1007/s002200050344.  Google Scholar

[21]

K. Kaneko, Globally coupled chaos violates the law of large numbers but not the central limit theorem, Phys. Rev. Lett., 12 (1990), 1391. doi: 10.1103/PhysRevLett.65.1391.  Google Scholar

[22]

C. Liverani and G. Keller, A spectral gap for a one-dimensional lattice of coupled piecewise expanding interval maps, in Dynamics of Coupled Map Lattices and of Related Spatially Extended Systems, Springer, 2005,115–151. doi: 10.1007/11360810_6.  Google Scholar

[23]

G. Keller, R. Klages and P. J. Howard, Continuity properties of transport coefficients in simple maps, Nonlinearity, 8 (2008), 1719. doi: 10.1088/0951-7715/21/8/003.  Google Scholar

[24]

G. Keller, An ergodic theoretic approach to mean field coupled maps, Prog. Probab., (2000), 183–208.  Google Scholar

[25]

S. Klus, P. Koltai and C. Schütte, On the numerical approximation of the Perron–Frobenius and Koopman operator, J. Comput. Dyn., 1 (2016), 51. doi: 10.3934/jcd.2016003.  Google Scholar

[26]

T-Y. Li, Finite approximation for the Frobenius–Perron operator. A solution to Ulam's conjecture, J. Approx Theory, 2 (1976), 177-186.  doi: 10.1016/0021-9045(76)90037-X.  Google Scholar

[27]

R. D. A. Murray, Discrete Approximation of Invariant Densities, University of Cambridge, 1997. Google Scholar

[28]

W. Parry, On the $\beta$-expansions of real numbers, Acta Math. Hung., 3-4 (1960), 401-416.  doi: 10.1007/BF02020954.  Google Scholar

[29]

W. Parry, Representations for real numbers, Acta Math. Hung., 1-2 (1964), 95-105.  doi: 10.1007/BF01897025.  Google Scholar

[30]

A. Rényi, Representations for real numbers and their ergodic properties, Acta Math. Hung., 3-4 (1957), 477-493.  doi: 10.1007/BF02020331.  Google Scholar

[31]

F. M. Sélley, Asymptotic Properties of Mean Field Coupled Maps, Ph.D thesis, Budapest University of Technology and Economics, 2019. Google Scholar

[32]

W. OttM. Stenlund and L-S. Young, Memory loss for time-dependent dynamical systems, Math. Res. Lett., 2 (2009), 463-475.  doi: 10.4310/MRL.2009.v16.n3.a7.  Google Scholar

[33]

S. M. Ulam, A Collection of Mathematical Problems, Interscience Tracts in Pure and Applied Mathematics, New York, 1960.  Google Scholar

Figure 1.  The choice of $ \beta_k $, $ k = 3 $ is pictured
Figure 2.  Approximation of $ \psi^{\varepsilon} $ with ergodic averages, $ F(x) = x $. $ P_{x, N}(\beta) $ is plotted for $ \beta \in [2, \beta_1] $ with gridsize $ \Delta $ and for $ x $ drawn uniform randomly from $ [0, 1] $. The line $ x = y $ is plotted
Figure 3.  Approximation of $ \psi^{\varepsilon} $ with ergodic averages. $ P_{x, N}(\beta) $ is plotted for $ \beta \in [2, 3] $ with gridsize $ \Delta = 10^{-4} $, $ N = 10^6 $ and for $ x $ drawn uniform randomly from $ [0, 1] $, $ \varepsilon = 0.8 $. The line $ x = y $ is plotted
Figure 4.  Approximation of $ \psi^{\varepsilon} $ with ergodic averages, $ F(x) = x^2 $. $ P_{x, N}(\beta) $ is plotted for $ \beta \in [2, \beta_1] $ with gridsize $ \Delta = 10^{-4} $, $ N = 10^6 $ and for $ x $ drawn uniform randomly from $ [0, 1] $. The line $ x = y $ is plotted
Figure 5.  Approximation of $ \psi^{\varepsilon} $ with ergodic averages, $ P_{x, N}(\beta) $ is plotted for $ \beta \in [2, 3] $ with gridsize $ \Delta = 10^{-4} $, $ N = 10^6 $ and for $ x $ drawn uniform randomly from $ [0, 1] $. The line $ x = y $ is plotted
Figure 6.  Total variation for each $ f_t^i $ as a function of time, $ F(x) = x $, $ T = 100 $, $ K_1\times K_2 = 100 $
Figure 7.  Associated slope to each $ f_t^i $ as a function of time, $ F(x) = x $, $ T = 100 $, $ K_1\times K_2 = 100 $
Figure 8.  Approximation of the invariant density of the self-consistent system (4) with $ F(x) = x $ by a high iterate of an appropriate initial density
Figure 9.  For each $ \varepsilon = k \cdot 10^{-3} $, $ k = 1, \dots, 10^3 $ the values $ \overline{\text{var}}(t) $ and $ \overline{\beta}(t) $ are plotted for $ t = 150, \dots, 200 $, $ F(x) = x $, $ K_1 \times K_2 = 100 $
Figure 10.  For each $ \varepsilon = k \cdot \Delta $, $ k = 1, \dots, E/\Delta $ the values $ \overline{\text{var}}(t) $ are plotted for $ t = 150, \dots, 200 $, $ K_1 \times K_2 = 100 $
Table 1.  Computation of the mean total variation $ \overline{\text{var}} $ and mean slope for $ F(x) = x $. $ T = 100 $, $ M = K_1 = K_2 = 10 $. Left hand side: $ \varepsilon = 0.1 $, right hand side: $ \varepsilon = 0.2 $
$t $ $\overline{\text{var}}$ $\overline{\beta}$ $t $ $\overline{\text{var}}$ $\overline{\beta}$
0 7.1815 2.0065 0 5.1121 1.9993
5 1.1544 2.0039 5 0.9983 2.0130
10 0.9559 2.0020 10 0.9415 2.0117
15 0.9856 2.0015 15 0.9490 2.0134
20 0.9901 2.0012 20 0.9429 2.0149
25 0.9917 2.0011 25 0.9371 2.0160
30 0.9928 2.0010 30 0.9341 2.0165
35 0.9934 2.0009 35 0.9323 2.0168
40 0.9940 2.0008 40 0.9330 2.0171
45 0.9944 2.0008 45 0.9320 2.0174
50 0.9947 2.0007 50 0.9309 2.0176
55 0.9949 2.0007 55 0.9298 2.0178
60 0.9950 2.0007 60 0.9289 2.0179
65 0.9952 2.0007 65 0.9280 2.0180
70 0.9953 2.0007 70 0.9274 2.0181
75 0.9954 2.0007 75 0.9270 2.0181
80 0.9954 2.0007 80 0.9269 2.0181
85 0.9955 2.0007 85 0.9269 2.0181
90 0.9955 2.0006 90 0.9269 2.0181
95 0.9956 2.0006 95 0.9270 2.0181
100 0.9956 2.0006 100 0.9270 2.0181
$t $ $\overline{\text{var}}$ $\overline{\beta}$ $t $ $\overline{\text{var}}$ $\overline{\beta}$
0 7.1815 2.0065 0 5.1121 1.9993
5 1.1544 2.0039 5 0.9983 2.0130
10 0.9559 2.0020 10 0.9415 2.0117
15 0.9856 2.0015 15 0.9490 2.0134
20 0.9901 2.0012 20 0.9429 2.0149
25 0.9917 2.0011 25 0.9371 2.0160
30 0.9928 2.0010 30 0.9341 2.0165
35 0.9934 2.0009 35 0.9323 2.0168
40 0.9940 2.0008 40 0.9330 2.0171
45 0.9944 2.0008 45 0.9320 2.0174
50 0.9947 2.0007 50 0.9309 2.0176
55 0.9949 2.0007 55 0.9298 2.0178
60 0.9950 2.0007 60 0.9289 2.0179
65 0.9952 2.0007 65 0.9280 2.0180
70 0.9953 2.0007 70 0.9274 2.0181
75 0.9954 2.0007 75 0.9270 2.0181
80 0.9954 2.0007 80 0.9269 2.0181
85 0.9955 2.0007 85 0.9269 2.0181
90 0.9955 2.0006 90 0.9269 2.0181
95 0.9956 2.0006 95 0.9270 2.0181
100 0.9956 2.0006 100 0.9270 2.0181
Table 2.  Computation of the mean total variation $ \overline{\text{var}} $ and mean slope for $ F(x) = x $, $ \text{var}(f^i_0) < 10^{-4} $. $ T = 300 $, $ M = K_1 = K_2 = 10 $. Left hand side: $ \varepsilon = 0.1 $, right hand side: $ \varepsilon = 0.2 $
$t $ $\overline{\text{var}}$ $\overline{\beta}$ $t $ $\overline{\text{var}}$ $\overline{\beta}$
0 0.0000 2.0000 0 0.0000 2.0000
25 1.0000 2.0000 25 1.0000 2.0000
50 1.0000 2.0000 50 0.9993 2.0003
75 1.0000 2.0000 75 0.9783 2.0072
100 0.9998 2.0000 100 0.9362 2.0160
125 0.9995 2.0001 125 0.9307 2.0177
150 0.9987 2.0002 150 0.9268 2.0181
175 0.9978 2.0003 175 0.9269 2.0181
200 0.9969 2.0005 200 0.9269 2.0181
225 0.9966 2.0005 225 0.9269 2.0181
250 0.9966 2.0005 250 0.9269 2.0181
275 0.9964 2.0005 275 0.9269 2.0181
300 0.9962 2.0006 300 0.9269 2.0181
$t $ $\overline{\text{var}}$ $\overline{\beta}$ $t $ $\overline{\text{var}}$ $\overline{\beta}$
0 0.0000 2.0000 0 0.0000 2.0000
25 1.0000 2.0000 25 1.0000 2.0000
50 1.0000 2.0000 50 0.9993 2.0003
75 1.0000 2.0000 75 0.9783 2.0072
100 0.9998 2.0000 100 0.9362 2.0160
125 0.9995 2.0001 125 0.9307 2.0177
150 0.9987 2.0002 150 0.9268 2.0181
175 0.9978 2.0003 175 0.9269 2.0181
200 0.9969 2.0005 200 0.9269 2.0181
225 0.9966 2.0005 225 0.9269 2.0181
250 0.9966 2.0005 250 0.9269 2.0181
275 0.9964 2.0005 275 0.9269 2.0181
300 0.9962 2.0006 300 0.9269 2.0181
Table 3.  Computation of $ \overline{\text{int}}_{f_*(\varepsilon)} $ and mean slope for $ F(x) = x $, $ T = 200 $, $ \bar{T} = 5000 $. $ M = K_1 = K_2 = 10 $
$t$ $\overline{\text{int}}_{f_*(0.1)}$ $\overline{\text{int}}_{f_*(0.2)}$
0 0.4096 0.5018
10 0.0101 0.0321
20 0.0049 0.0174
30 0.0028 0.0097
40 0.0017 0.0064
50 0.0011 0.0040
60 0.0007 0.0021
70 0.0005 0.0007
80 0.0004 0.0003
90 0.0003 0.0002
100 0.0002 0.0001
110 0.0002 0.0001
120 0.0002 0.0001
130 0.0001 0.0001
140 0.0001 0.0001
150 0.0001 0.0001
160 0.0001 0.0001
170 0.0001 0.0001
180 0.0001 0.0001
190 0.0001 0.0001
200 0.0001 0.0001
$t$ $\overline{\text{int}}_{f_*(0.1)}$ $\overline{\text{int}}_{f_*(0.2)}$
0 0.4096 0.5018
10 0.0101 0.0321
20 0.0049 0.0174
30 0.0028 0.0097
40 0.0017 0.0064
50 0.0011 0.0040
60 0.0007 0.0021
70 0.0005 0.0007
80 0.0004 0.0003
90 0.0003 0.0002
100 0.0002 0.0001
110 0.0002 0.0001
120 0.0002 0.0001
130 0.0001 0.0001
140 0.0001 0.0001
150 0.0001 0.0001
160 0.0001 0.0001
170 0.0001 0.0001
180 0.0001 0.0001
190 0.0001 0.0001
200 0.0001 0.0001
Table 4.  Computation of the mean total variation $ \overline{\text{var}} $. $ T = 100 $, $ M = K_1 = K_2 = 10 $. Left hand side: $ F(x) = x^2 $, center: $ F(x) = x^4 $, right hand side: $ F(x) = x^6 $
$t $ $ \varepsilon=1 $ $ \varepsilon=2.5$ $t $ $\varepsilon=1 $ $ \varepsilon=35$ $t$ $\varepsilon=1$ $ \varepsilon=400 $
0 6.4694 5.0341 0 5.3766 6.4806 0 7.7582 5.1444
5 1.2483 1.1951 5 1.2370 1.1785 5 1.3852 1.0419
10 0.9808 0.8849 10 0.9882 0.9788 10 0.5224 0.5990
15 0.9914 0.8471 15 0.6884 0.9873 15 0.0210 0.1435
20 0.9976 0.8447 20 0.0474 0.5994 20 0.0007 0.0026
25 0.9994 0.8275 25 0.0015 0.1093 25 0.0000 0.0002
30 0.9999 0.8418 30 0.0000 0.0347 30 0.0000 0.0000
35 1.0000 0.8305 35 0.0000 0.0023 35 0.0000 0.0000
40 1.0000 0.8468 40 0.0000 0.0001 40 0.0000 0.0000
45 1.0000 0.8321 45 0.0000 0.0000 45 0.0000 0.0000
50 0.9531 0.8358 50 0.0000 0.0000 50 0.0000 0.0000
55 0.5357 0.8382 55 0.0000 0.0000 55 0.0000 0.0000
60 0.1802 0.7651 60 0.0000 0.0000 60 0.0000 0.0000
65 0.0128 0.6366 65 0.0000 0.0000 65 0 0.0000
70 0.0004 0.5117 70 0.0000 0.0000 70 0 0
75 0.0000 0.4535 75 0 0.0000 75 0 0
80 0.0000 0.4269 80 0 0.0000 80 0 0
85 0.0000 0.4255 85 0 0 85 0 0
90 0.0000 0.4015 90 0 0 90 0 0
95 0.0000 0.3966 95 0 0 95 0 0
100 0.0000 0.3813 100 0 0 100 0 0
$t $ $ \varepsilon=1 $ $ \varepsilon=2.5$ $t $ $\varepsilon=1 $ $ \varepsilon=35$ $t$ $\varepsilon=1$ $ \varepsilon=400 $
0 6.4694 5.0341 0 5.3766 6.4806 0 7.7582 5.1444
5 1.2483 1.1951 5 1.2370 1.1785 5 1.3852 1.0419
10 0.9808 0.8849 10 0.9882 0.9788 10 0.5224 0.5990
15 0.9914 0.8471 15 0.6884 0.9873 15 0.0210 0.1435
20 0.9976 0.8447 20 0.0474 0.5994 20 0.0007 0.0026
25 0.9994 0.8275 25 0.0015 0.1093 25 0.0000 0.0002
30 0.9999 0.8418 30 0.0000 0.0347 30 0.0000 0.0000
35 1.0000 0.8305 35 0.0000 0.0023 35 0.0000 0.0000
40 1.0000 0.8468 40 0.0000 0.0001 40 0.0000 0.0000
45 1.0000 0.8321 45 0.0000 0.0000 45 0.0000 0.0000
50 0.9531 0.8358 50 0.0000 0.0000 50 0.0000 0.0000
55 0.5357 0.8382 55 0.0000 0.0000 55 0.0000 0.0000
60 0.1802 0.7651 60 0.0000 0.0000 60 0.0000 0.0000
65 0.0128 0.6366 65 0.0000 0.0000 65 0 0.0000
70 0.0004 0.5117 70 0.0000 0.0000 70 0 0
75 0.0000 0.4535 75 0 0.0000 75 0 0
80 0.0000 0.4269 80 0 0.0000 80 0 0
85 0.0000 0.4255 85 0 0 85 0 0
90 0.0000 0.4015 90 0 0 90 0 0
95 0.0000 0.3966 95 0 0 95 0 0
100 0.0000 0.3813 100 0 0 100 0 0
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