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Optimal quantization for a probability measure on a nonuniform stretched Sierpiński triangle

  • *Corresponding author: Megha Pandey

    *Corresponding author: Megha Pandey 
Abstract / Introduction Full Text(HTML) Figure(5) / Table(1) Related Papers Cited by
  • Quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with support containing a finite number of elements. In this paper, we have considered a Borel probability measure $ P $ on $ \mathbb R^2 $, which has support a nonuniform stretched Sierpiński triangle generated by a set of three contractive similarity mappings on $ \mathbb R^2 $. For this probability measure, we investigate the optimal sets of $ n $-means and the $ n $th quantization errors for all positive integers $ n $.

    Mathematics Subject Classification: 60E05, 28A80, 94A34.

    Citation:

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  • Figure 1.  Some basic triangles with their vertices that construct the stretched Sierpiński triangle

    Figure 2.  Optimal configuration of $ n $ points for $ 1\leq n\leq 6 $

    Figure 3.  Optimal configuration of $ n $ points for $ n = 7 $

    Figure 4.  Optimal configuration of $ n $ points for $ n = 8 $

    Figure 5.  Tree diagram of the optimal sets from $ \alpha_8 $ to $ \alpha_{21} $

    Table 1.  Number of $ \alpha_n $ in the range $ 5\leq n\leq 82 $

    $ n $ $ \text{ card }(\mathcal{C}_n) $ $ n $ $ \text{ card }(\mathcal{C}_n) $ $ n $ $ \text{ card }(\mathcal{C}_n) $ $ n $ $ \text{ card }(\mathcal{C}_n) $ $ n $ $ \text{ card }(\mathcal{C}_n) $ $ n $ $ \text{ card }(\mathcal{C}_n) $
    5 1 18 4 31 6 44 1 57 495 70 56
    6 1 19 6 32 4 45 8 58 792 71 28
    7 2 20 4 33 1 46 28 59 924 72 8
    8 1 21 1 34 6 47 56 60 792 73 1
    9 1 22 1 35 15 48 70 61 495 74 1
    10 2 23 6 36 20 49 56 62 220 75 12
    11 1 24 15 37 15 50 28 63 66 76 66
    12 1 25 20 38 6 51 8 64 12 77 220
    13 4 26 15 39 1 52 1 65 1 78 495
    14 6 27 6 40 1 53 1 66 8 79 792
    15 4 28 1 41 4 54 12 67 28 80 924
    16 1 29 1 42 6 55 66 68 56 81 792
    17 1 30 4 43 4 56 220 69 70 82 495
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  • [1] E. F. Abaya and G. L. Wise, Some remarks on the existence of optimal quantizers, Statistics & Probability Letters, 2 (1984), 349-351. 
    [2] P. BitengM. CaguiatD. DebM. K. Roychowdhury and B. Villanueva, Constrained quantization for a uniform distribution, Houston Journal of Mathematics, 50 (2024), 121-142. 
    [3] P. BitengM. CaguiatT. Dominguez and M. K. Roychowdhury, Conditional quantization for uniform distributions on line segments and regular polygons, Mathematics, 13 (2025), 1024.  doi: 10.3390/math13071024.
    [4] D. Cömez and M. K. Roychowdhury, Quantization for uniform distributions on stretched Sierpiński triangles, Monatshefte für Mathematik, 190 (2019), 79-100.  doi: 10.1007/s00605-019-01314-5.
    [5] D. Cömez and M. K. Roychowdhury, Quantization for uniform distributions of Cantor dusts on $\mathbb{R}^2$, Topology Proceedings, 56 (2020), 195-218. 
    [6] C. P. Dettmann and M. K. Roychowdhury, Quantization for uniform distributions on equilateral triangles, Real Analysis Exchange, 42 (2017), 149-166.  doi: 10.14321/realanalexch.42.1.0149.
    [7] Q. DuV. Faber and M. Gunzburger, Centroidal Voronoi tessellations: Applications and algorithms, SIAM Review, 41 (1999), 637-676.  doi: 10.1137/S0036144599352836.
    [8] A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, volume 159. Springer Science & Business Media, 2012.
    [9] S. Graf and H. Luschgy, The quantization of the Cantor distribution, Mathematische Nachrichten, 183 (1997), 113-133.  doi: 10.1002/mana.19971830108.
    [10] S. Graf and H. Luschgy, Quantization for probability measures with respect to the geometric mean error, Mathematical Proceedings of the Cambridge Philosophical Society, 136 (2004), 687-717.  doi: 10.1017/S0305004103007229.
    [11] S. Graf and H. Luschgy, Foundations of Quantization for Probability Distributions, Lecture Notes in Math., 1730. Springer-Verlag, Berlin, 2000.
    [12] R. M. GrayJ. C. Kieffer and Y. Linde, Locally optimal block quantizer design, Information and Control, 45 (1980), 178-198.  doi: 10.1016/S0019-9958(80)90313-7.
    [13] R. M. Gray and D. L. Neuhoff, Quantization, IEEE Transactions on Information Theory, 44 (1998), 2325-2383.  doi: 10.1109/18.720541.
    [14] A. Gyorgy and T. Linder, On the structure of optimal entropy-constrained scalar quantizers, IEEE Transactions on Information Theory, 48 (2002), 416-427.  doi: 10.1109/18.978755.
    [15] C. Hamilton, E. Nyanney, M. Pandey and M. K. Roychowdhury, Conditional constrained and unconstrained quantization for uniform distributions on regular polygons, Real Analysis Exchange., (2025), 1-45. doi: 10.14321/realanalexch.1725509532.
    [16] M. KesseböhmerA. Niemann and S. Zhu, Quantization dimensions of compactly supported probability measures via Rényi dimensions, Transactions of the American Mathematical Society, 376 (2023), 4661-4678.  doi: 10.1090/tran/8863.
    [17] M. Pandey and M. K. Roychowdhury., Conditional constrained and unconstrained quantization for probability distributions, arXiv: 2312.02965.
    [18] M. Pandey and M. K. Roychowdhury, Constrained quantization for a uniform distribution with respect to a family of constraints, arXiv: 2309.11498.
    [19] M. Pandey and M. K. Roychowdhury, Constrained quantization for the Cantor distribution, Journal of Fractal Geometry, 11 (2024), 319-341.  doi: 10.4171/jfg/147.
    [20] M. Pandey and M. K. Roychowdhury, Constrained quantization for the Cantor distribution with a family of constraints, arXiv preprint, arXiv: 2401.01958, (2024).
    [21] M. Pandey and M. K. Roychowdhury, Constrained quantization for probability distributions, Journal of Fractal Geometry, (2025).
    [22] D. Pollard, Quantization and the method of $k$-means, IEEE Transactions on Information Theory, 28 (1982), 199-205.  doi: 10.1109/TIT.1982.1056481.
    [23] K. Pötzelberger, The quantization dimension of distributions, Mathematical Proceedings of the Cambridge Philosophical Society, 131 (2001), 507-519.  doi: 10.1017/S0305004101005357.
    [24] L. Roychowdhury, Optimal quantization for nonuniform Cantor distributions, Journal of Interdisciplinary Mathematics, 22 (2019), 1325-1348.  doi: 10.1080/09720502.2019.1698401.
    [25] M. K. Roychowdhury, Quantization and centroidal Voronoi tessellations for probability measures on dyadic Cantor sets, Journal of Fractal Geometry, 4 (2017), 127-146.  doi: 10.4171/jfg/47.
    [26] M. K. Roychowdhury, Least upper bound of the exact formula for optimal quantization of some uniform Cantor distribution, Discrete & Continuous Dynamical Systems: Series A, 38 (2018).
    [27] M. K. Roychowdhury, Optimal quantization for the Cantor distribution generated by infinite similutudes, Israel Journal of Mathematics, 231 (2019), 437-466.  doi: 10.1007/s11856-019-1859-5.
    [28] M. K. Roychowdhury, Optimal quantizers for a nonuniform distribution on a Sierpiński carpet, International Conference on Nonlinear Applied Analysis and Optimization, (2021), 43-62. doi: 10.1007/978-981-99-0597-3_5.
    [29] M. K. Roychowdhury and B. Selmi, Local dimensions and quantization dimensions in dynamical systems, The Journal of Geometric Analysis, 31 (2021), 6387-6409.  doi: 10.1007/s12220-020-00537-5.
    [30] P. Zador, Asymptotic quantization error of continuous signals and the quantization dimension, IEEE Transactions on Information Theory, 28 (1982), 139-149.  doi: 10.1109/TIT.1982.1056490.
    [31] R. ZamirLattice Coding for Signals and Networks: A Structured Coding Approach to Quantization, Modulation, and Multiuser Information Theory, Cambridge University Press, 2014. 
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