{"id":4333,"date":"2016-12-02T14:32:40","date_gmt":"2016-12-02T11:32:40","guid":{"rendered":"https:\/\/hpc-education.unn.ru\/?page_id=4333"},"modified":"2017-12-19T14:58:10","modified_gmt":"2017-12-19T11:58:10","slug":"publications","status":"publish","type":"page","link":"https:\/\/hpc-education.unn.ru\/en\/research\/overview\/global-optimization\/publications","title":{"rendered":"\u041f\u0443\u0431\u043b\u0438\u043a\u0430\u0446\u0438\u0438"},"content":{"rendered":"

\u041f\u043e \u043f\u0435\u0440\u0432\u043e\u043c\u0443 \u044d\u0442\u0430\u043f\u0443 \u043f\u0440\u043e\u0435\u043a\u0442\u0430<\/h2>\n
    \n
  1. Andrey Pepelyshev and Anatoly Zhigljavsky. SSA analysis and forecasting of records for Earth temperature and ice extents<\/a>. Statistics and Its Interface. 10, No.1, pp. 151-163, 2017.
    \nDOI: 10.4310\/SII.2017.v10.n1.a14
    \nAccession Number WoS: 000386413100015
    \nScopus EID: 2-s2.0-85008219560<\/li>\n
  2. Andrey Pepelyshev, Yuri Staroselskiy, and Anatoly Zhigljavsky. Adaptive targeting for online Advertisement<\/a>. LNCS 9432, pp. 240\u2013251, 2015.
    \nDOI: 10.1007\/978-3-319-27926-8_21
    \nScopus EID: 2-s2.0-84955269375<\/li>\n
  3. Daniela Lera, Yaroslav D. Sergeyev. Space-filling curves and multiple estimates of H\u00f6lder constants in derivative-free global optimization<\/a>. AIP Conference Proceedings, 1738, 400008, 2016.
    \nDOI: 10.1063\/1.4952196
    \nAccession Number WoS: 000380803300419
    \nScopus EID: 2-s2.0-84984555025<\/li>\n
  4. Dmitri E. Kvasov, Marat S. Mukhametzhanov. One-Dimensional Global Search: Nature-Inspired vs. Lipschitz Methods<\/a>. AIP Conference Proceedings, 1738, 400012, 2016.
    \nDOI: 10.1063\/1.4952200
    \nAccession Number WoS: 000380803300423
    \nScopus EID: 2-s2.0-84984559431<\/li>\n
  5. Jonathan Gillard, Anatoly Zhigljavsky. Global optimization for structured low rank approximation<\/a>. AIP Conference Proceedings,  1738, 400003, 2016.
    \nDOI: 10.1063\/1.4952191
    \nAccession Number WoS: 000380803300414
    \nScopus EID: 2-s2.0-84984549652<\/li>\n
  6. Konstantin Barkalov, Victor Gergel, Ilya Lebedev. Solving Global Optimization Problems on GPU Cluster<\/a>. AIP Conference Proceedings, 1738, 400006, 2016.
    \nDOI: 10.1063\/1.4952194
    \nAccession Number WoS: 000380803300417
    \nScopus EID: 2-s2.0-84984559157<\/li>\n
  7. Mauro Brunato and Roberto Battiti. Stochastic Local Search for Direct Training of Threshold Networks<\/a>. Proc. 2015 International Joint Conference on Neural Networks, IEEE Publisher, pp. 1-8, 2015. 
    \nDOI: 10.1109\/IJCNN.2015.7280770
    \nScopus EID: 2-s2.0-84950996840<\/li>\n
  8. Modorskii V.Ya., Gaynutdinova D.F., Gergel V.P., Barkalov K.A. Optimization in Design of Scientific Products for Purposes of Cavitation Problems<\/a>. AIP Conference Proceedings, 1738, 400013, 2016.
    \nDOI: 10.1063\/1.4952201
    \nAccession Number WoS: 000380803300424
    \nScopus EID: 2-s2.0-84984549606<\/li>\n
  9. Victor Gergel, Vladimir Grishagin, Alexander Gergel. Adaptive nested optimization scheme for multidimensional global search.<\/a> Journal of Global Optimization, Vol. 66, No.1, pp. 35\u201351, 2016. 
    \nDOI: 10.1007\/s10898-015-0355-7
    \nAccession Number WoS:
    \nScopus EID:<\/li>\n
  10. Vladimir A. Grishagin, Ruslan A. Israfilov. Global Search Acceleration in the Nested Optimization Scheme<\/a>. AIP Conference Proceedings, 1738, 400010, 2016.
    \nDOI: 10.1063\/1.4952198
    \nAccession Number WoS: 000380803300421
    \nScopus EID: 2-s2.0-84984539098<\/li>\n
  11. Vladimir Grishagin and Ruslan Israfilov. Multidimensional Constrained Global Optimization in Domains with Computable Boundaries<\/a>. CEUR Workshop Proceedings, 1513, pp. 75-84, 2015.
    \nScopus EID: 2-s2.0-84960949984<\/li>\n
  12. Yaroslav D. Sergeyev, Dmitri E. Kvasov, Marat S. Mukhametzhanov. Comments upon the Usage of Derivatives in Lipschitz Global Optimization<\/a>. AIP Conference Proceedings,  1738, 400004, 2016.
    \nDOI:  10.1063\/1.4952192
    \nAccession Number WoS: 000380803300415
    \nScopus EID: 2-s2.0-84984545986<\/li>\n
  13. Yaroslav D. Sergeyev, Marat S. Mukhametzhanov, Dmitri E. Kvasov, Daniela Lera. Derivative-Free Local Tuning and Local Improvement Techniques Embedded in the Univariate Global Optimization<\/a>. Journal of Optimization Theory and Applications, Vol. 171, 1, pp 186\u2013208, 2016.
    \nDOI: 10.1007\/s10957-016-0947-5
    \nAccession Number WoS: 000383577500009
    \nScopus EID: 2-s2.0-84968593869<\/li>\n<\/ol>\n

    \u041f\u043e \u0432\u0442\u043e\u0440\u043e\u043c\u0443 \u044d\u0442\u0430\u043f\u0443 \u043f\u0440\u043e\u0435\u043a\u0442\u0430<\/h2>\n
      \n
    1. Afraimovich L.G., Katerov A.S., Prilutskii M.Kh. Multi-Index Traffic Problems with 1-Nested Structure<\/a>. Automation and Remote Control, 77, No. 11, pp. 1894\u20131913, 2016.
      \nDOI: 10.1134\/S0005117916110023
      \nScopus EID: 2-s2.0-8499473060<\/li>\n
    2. Barkalov K.A., Gergel V.P. Parallel global optimization on GPU<\/a>. Journal of Global Optimization, 66, No. 1, pp. 3\u201320, 2016.
      \nDOI: 10.1007\/s10898-016-0411-y
      \nAccession Number WoS: 000382141100002
      \nScopus EID: 2-s2.0-84957945752<\/li>\n
    3. Barkalov K., Lebedev I. Local tuning in multilevel scheme of parallel global optimization<\/a>. AIP Conference Proceedings, Vol. 1776, pp. 060006-1 – 060006-4, 2016.
      \nDOI: 10.1063\/1.4965340
      \nScopus EID: 2-s2.0-84995460849<\/li>\n
    4. Barkalov K.A., Lebedev I.G. Solving Multidimensional Global Optimization Problems Using Graphics Accelerators<\/a>. Communications in Computer and Information Science, 
      \nDOI: 10.1007\/978-3-319-55669-7_18<\/li>\n
    5. \u0411\u0430\u0440\u043a\u0430\u043b\u043e\u0432 \u041a.\u0410., \u041b\u0435\u0431\u0435\u0434\u0435\u0432 \u0418.\u0413. \u0420\u0435\u0448\u0435\u043d\u0438\u0435 \u0437\u0430\u0434\u0430\u0447 \u0433\u043b\u043e\u0431\u0430\u043b\u044c\u043d\u043e\u0439 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0443\u0441\u043a\u043e\u0440\u0438\u0442\u0435\u043b\u044f\u0445.<\/a> \u0421\u0443\u043f\u0435\u0440\u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u043d\u044b\u0435 \u0434\u043d\u0438 \u0432 \u0420\u043e\u0441\u0441\u0438\u0438: \u0422\u0440\u0443\u0434\u044b \u043c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u043e\u0439 \u043a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u0438 (26-27 \u0441\u0435\u043d\u0442\u044f\u0431\u0440\u044f 2016 \u0433., \u0433. \u041c\u043e\u0441\u043a\u0432\u0430), \u0441. 640-650, 2016.<\/li>\n
    6. Barkalov K.A., Sysoyev A.V., Lebedev I.G., Sovrasov V.V. Solving GENOPT Problems with the Use of ExaMin Solver<\/a>. Lecture Notes in Computer Science, Vol. 10079, pp. 283-295, 2016.
      \nDOI: 10.1007\/978-3-319-50349-3_24<\/li>\n
    7. Battiti R., Sergeyev Ya.D., Brunato M., Kvasov D.E. GENOPT 2016: Design of a generalization-based challenge in global optimization<\/a>. AIP Conference Proceedings, Vol. 1776, pp. 060005-1 – 060005-4, 2016.
      \nDOI: 10.1063\/1.4965339
      \nScopus EID: 2-s2.0-84995494155<\/li>\n
    8. Brunato M., Battiti R. A Telescopic Binary Learning Machine for Training Neural Networks<\/a>. IEEE Transactions on Neural Networks and Learning Systems, Vol. PP, No.99, pp. 1-13, 2016.
      \nDOI: 10.1109\/TNNLS.2016.2537300
      \nScopus EID: 2-s2.0-84961825602<\/li>\n
    9. Gillard J.W., Kvasov D. E. Lipschitz optimization methods for fitting a sum of damped sinusoids to a series of observations.<\/a> Statistics and Its Interface, Volume 10, No. 1, pp. 59\u201370, 2017.
      \nDOI: 10.4310\/SII.2017.v10.n1.a6
      \nAccession Number WoS: 000386413100007<\/li>\n
    10. Gillard J., Kvasov D., Zhigljavsky A. Optimization Problems in Structured Low Rank Approximation<\/a>. AIP Conference Proceedings, Vol. 1776, pp. 060004-1 – 060004-4, 2016.
      \nDOI: 10.1063\/1.4965338
      \nScopus EID: 2-s2.0-84995449819<\/li>\n
    11. Grishagin V.A., Israfilov R.A., Sergeyev Ya.D. Comparative Efficiency of Dimensionality Reduction Schemes in Global Optimization<\/a>. AIP Conference Proceedings, Vol. 1776, pp. 060011-1 – 060011-4, 2016.
      \nDOI: 10.1063\/1.4965345
      \nScopus EID: 2-s2.0-84995520651<\/li>\n
    12. Zhigljavsky A., Golyandina N., Gryaznov S. Deconvolution of a discrete uniform distribution<\/a>. Statistics and Probability Letters, Vol. 118, pp. 37-44, 2016.
      \nDOI: 10.1016\/j.spl.2016.06.006
      \nAccession Number WoS: 000381839500006
      \nScopus EID: 2-s2.0-84976908752<\/li>\n
    13. \u017dilinskas A., Zhigljavsky A. Stochastic Global Optimization: A Review on the Occasion of 25 Years of Informatica<\/a>. Informatica, Vol. 27, No. 2, pp. 229\u2013256, 2016.
      \nDOI: 10.15388\/Informatica.2016.83
      \nAccession Number WoS: 000379100600001
      \nScopus EID: 2-s2.0-84987948207<\/li>\n
    14. Cococcioni M., Pappalardo M., Sergeyev Ya.D. Towards lexicographic multi-objective linear programming using grossone methodology<\/a>. AIP Conference Proceedings, Vol. 1776, pp. 090040-1 – 090040-5, 2016.
      \nDOI: 10.1063\/1.4965404
      \nScopus EID: 2-s2.0-84995520586<\/li>\n
    15. Lera D., Sergeyev Ya.D. Remarks on Global Optimization Using Space-Filling Curves<\/a>. AIP Conference Proceedings, Vol. 1776, pp. 060010-1 – 060010-4, 2016.
      \nDOI: 10.1063\/1.4965344
      \nScopus EID: 2-s2.0-84995467847<\/li>\n
    16. Mazzia F., Sergeyev Ya.D., Iavernaro F., Amodio P., Mukhametzhanov M.S. Numerical methods for solving ODEs on the infinity computer<\/a>. AIP Conference Proceedings, Vol. 1776, pp. 090033-1 – 090033-4, 2016.
      \nDOI: 10.1063\/1.4965397
      \nScopus EID: 2-s2.0-84995475415<\/li>\n
    17. Sergeyev Ya.D. The exact (up to infinitesimals) infinite perimeter of the Koch snowflake and its finite area<\/a>. Communications in Nonlinear Science and Numerical Simulation, Vol. 31, pp. 21-29, 2016.
      \nDOI: 10.1016\/j.cnsns.2015.07.004
      \nAccession Number WoS: 0003620468000037
      \nScopus EID: 2-s2.0-84940476954<\/li>\n
    18. Sergeyev Ya.D., Kvasov D.E. Deterministic Global Optimization. An Introduction to the Diagonal Approach<\/a>. Springer, 2017.<\/li>\n
    19. Sergeyev Ya.D., Kvasov D.E., Mukhametzhanov M.S. On the Least-Squares Fitting of Data by Sinusoids<\/a>. Advances in Stochastic and Deterministic Global Optimization, Series Springer Optimization and Its Applications, Springer International Publishing, Vol.107, pp. 209-226, 2016.
      \nDOI: 10.1007\/978-3-319-29975-4_11
      \nScopus EID: 2-s2.0-84994479867<\/li>\n
    20. Sergeyev Ya.D., Kvasov D.E., Mukhametzhanov M.S., De Franco A. Acceleration Techniques in the Univariate Lipschitz Global Optimization<\/a>. AIP Conference Proceedings, Vol.1776, pp. 090051-1 – 090051-4, 2016.
      \nDOI: 10.1063\/1.4965415
      \nScopus EID: 2-s2.0-84995475808<\/li>\n
    21. Sergeyev Ya.D., Mukhametzhanov M.S., Mazzia F., Iavernaro F., Amodio P. Numerical Methods for Solving Initial Value Problems on the Infinity Computer<\/a>. International Journal of Unconventional Computing, Vol. 12, No. 1, pp. 3\u201323, 2016.
      \nAccession Number WoS: 000376156600002
      \nScopus EID: 2-s2.0-84969277810<\/li>\n<\/ol>\n

      \u041f\u043e \u0442\u0440\u0435\u0442\u044c\u0435\u043c\u0443 \u044d\u0442\u0430\u043f\u0443 \u043f\u0440\u043e\u0435\u043a\u0442\u0430<\/h2>\n

      \u041c\u043e\u043d\u043e\u0433\u0440\u0430\u0444\u0438\u0438<\/strong><\/p>\n

        \n
      1. Golyandina N., Korobeynikov A., Zhigljavsky A. Singular Spectrum Analysis with R. Springer, Heidelberg. 296 p. (\u043f\u0440\u0438\u043d\u044f\u0442\u0430 \u043a \u043f\u0443\u0431\u043b\u0438\u043a\u0430\u0446\u0438\u0438)<\/li>\n
      2. Battiti R., Kvasov D., Sergeyev Y. (eds). Learning and Intelligent Optimization.11th International Conference, LION 11, Nizhny Novgorod, Russia, June 19-21, 2017, Revised Selected Papers<\/a>. Lecture Notes in Computer Science, vol 10556. Springer, Cham . 403 pp. (XIII + 390)
        \nDOI: 10.1007\/978-3-319-69404-7<\/li>\n<\/ol>\n

        \u0421\u0442\u0430\u0442\u044c\u0438<\/strong><\/p>\n

          \n
        1. Vladimir Grishagin, Ruslan Israfilov, Yaroslav Sergeyev. Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes<\/a>. Applied Mathematics and Computation (2018) vol. 318, pp.  270\u2013280
          \nDOI: 10.1016\/j.amc.2017.06.036
          \nScopus EID: 2-s2.0-85025428828
          \nAccession Number WoS:  000415905500019<\/li>\n
        2. Yaroslav D. Sergeyev, Dmitri E. Kvasov, Marat S. Mukhametzhanov. Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms<\/a>. Mathematics and Computers in Simulation (2017) vol. 141, pp.  96\u2013109.
          \nDOI: 10.1016\/j.matcom.2016.05.006
          \nAccession Number WoS: 000408783100009
          \nScopus EID: 2-s2.0-84977633110<\/li>\n
        3. Dmitri E. Kvasov, Marat S. Mukhametzhanov. Metaheuristic vs. deterministic global optimization algorithms: The univariate case<\/a>. Applied Mathematics and Computation 318 (2018) 245\u2013259.
          \nDOI:10.1016\/j.amc.2017.05.014
          \nAccession Number WoS: 000415905500017
          \nScopus EID: 2-s2.0-85019578331<\/li>\n
        4. Marco Cococcioni, Massimo Pappalardo , Yaroslav D. Sergeyev. Lexicographic multi-objective linear programming using grossone methodology: Theory and algorithm<\/a>. Applied Mathematics and Computation 318 (2018) 298\u2013311.
          \nDOI:10.1016\/j.amc.2017.05.058
          \nAccession Number WoS: 000415905500022
          \nScopus EID: 2-s2.0-85020121970<\/li>\n
        5. Jonathan Gillard, Anatoly Zhigljavsky. Optimal estimation of direction in regression models with large number of parameters<\/a>. Applied Mathematics and Computation 318 (2018) 281\u2013289.
          \nDOI:10.1016\/j.amc.2017.05.050
          \nAccession Number WoS: 000415905500020
          \nScopus EID: 2-s2.0-85020019797<\/li>\n
        6. Gillard J., Zhigljavsky A. (2017) Global Optimization Challenges in Structured Low Rank Approximation<\/a>. In: Battiti R., Kvasov D., Sergeyev Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science, vol 10556. Pp. 326\u2013330. Springer, Cham
          \nDOI:10.1007\/978-3-319-69404-7_26
          \nScopus: 2-s2.0-85034236220<\/li>\n
        7. Pepelyshev A., Kornikov V., Zhigljavsky A. (2017) Statistical Estimation in Global Random Search Algorithms in Case of Large Dimensions<\/a>. In: Battiti R., Kvasov D., Sergeyev Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science, vol 10556. Pp. 364-369. Springer, Cham
          \nDOI:10.1007\/978-3-319-69404-7_32
          \nScopus EID: 2-s2.0-85034237755<\/li>\n
        8. Renato De Leone, Giovanni Fasano,\u00b7Yaroslav D. Sergeyev. Planar methods and grossone for the Conjugate Gradient breakdown in nonlinear programming<\/a>. Computational Optimization and Applications (2017)
          \nDOI 10.1007\/s10589-017-9957-y
          \nScopus EID: 2-s2.0-85031939975<\/li>\n
        9. Manlio Gaudioso, Giovanni Giallombardo, Marat Mukhametzhanov. Numerical infinitesimals in a variable metric method for convex nonsmooth optimization<\/a>. Applied Mathematics and Computation 318 (2018) 312\u2013320
          \nDOI: 10.1016\/j.amc.2017.07.057
          \nAccession Number WoS: 000415905500023
          \nScopus EID: 2-s2.0-85026870535<\/li>\n
        10. Sergeyev Y.D., Kvasov D.E., Mukhametzhanov M.S. (2017) Emmental-Type GKLS-Based Multiextremal Smooth Test Problems with Non-linear Constraints<\/a>. In: Battiti R., Kvasov D., Sergeyev Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science, vol 10556, pp. 383\u2013388. Springer, Cham
          \nDOI:10.1007\/978-3-319-69404-7_35
          \nScopus EID: 2-s2.0-85034254302<\/li>\n
        11. Pepelyshev, A., Zhigljavsky, A.,\u017dilinskas, A. Performance of global random search algorithms for large dimensions<\/a>. J Glob Optim (2017) pp.1-15.
          \nDOI: 10.1007\/s10898-017-0535-8
          \nScopus EID: 2-s2.0-85020083322<\/li>\n
        12. Daniela Lera, Yaroslav D. Sergeyev. GOSH: derivative-free global optimization using multi-dimensional space-filling curves<\/a>. Journal of Global Optimization (2017).
          \nDOI: 10.1007\/s10898-017-0589-7<\/li>\n
        13. Yaroslav D. Sergeyev, Dmitri E. Kvasov, Marat S. Mukhametzhanov. On strong homogeneity of a class of global optimization algorithms working with infinite and infinitesimal scales<\/a>. Communications in Nonlinear Science and Numerical Simulation, vol. 59 (2018) pp. 319-330.
          \nDOI: 10.1016\/j.cnsns.2017.11.013<\/li>\n
        14. Tahir Emre Kalaici, Roberto Battiti. A reactive self-tuning scheme for multilevel graph partitioning<\/a>. Applied Mathematics and Computation, vol. 318, pp. 227-244. 2018.
          \nDOI: 10.1016\/j.amc.2017.08.031
          \nAccession Number WoS: 000415905500016
          \nScopus EID:  2-s2.0-85029290402<\/li>\n
        15. \u0410\u0444\u0440\u0430\u0439\u043c\u043e\u0432\u0438\u0447 \u041b.\u0413. \u041d\u0438\u0436\u043d\u044f\u044f \u043e\u0446\u0435\u043d\u043a\u0430 \u0434\u043b\u044f \u0442\u0440\u0435\u0445\u0438\u043d\u0434\u0435\u043a\u0441\u043d\u043e\u0439 \u0430\u043a\u0441\u0438\u0430\u043b\u044c\u043d\u043e\u0439 \u0437\u0430\u0434\u0430\u0447\u0438 \u043e \u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u0445 \u0441 1,2-\u0434\u0435\u043a\u043e\u043c\u043f\u043e\u0437\u0438\u0446\u0438\u043e\u043d\u043d\u043e\u0439 \u043c\u0430\u0442\u0440\u0438\u0446\u0435\u0439 \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u0435\u0439<\/a>. \u0412\u0435\u0441\u0442\u043d\u0438\u043a \u0412\u043e\u043b\u0436\u0441\u043a\u043e\u0439 \u0433\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0439 \u0430\u043a\u0430\u0434\u0435\u043c\u0438\u0438 \u0432\u043e\u0434\u043d\u043e\u0433\u043e \u0442\u0440\u0430\u043d\u0441\u043f\u043e\u0440\u0442\u0430. \u0412\u044b\u043f\u0443\u0441\u043a 49, \u0441. 25-29. 2017<\/li>\n
        16. \u0413\u0440\u0438\u0448\u0430\u0433\u0438\u043d \u0412.\u0410., \u0418\u0441\u0440\u0430\u0444\u0438\u043b\u043e\u0432 \u0420.\u0410. \u041f\u0430\u0440\u0430\u043b\u043b\u0435\u043b\u044c\u043d\u0430\u044f \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0430\u0434\u0430\u043f\u0442\u0438\u0432\u043d\u043e\u0439 \u043c\u043d\u043e\u0433\u043e\u0448\u0430\u0433\u043e\u0432\u043e\u0439 \u0441\u0445\u0435\u043c\u044b \u0440\u0435\u0434\u0443\u043a\u0446\u0438\u0438 \u0440\u0430\u0437\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u0438 \u0434\u043b\u044f \u0437\u0430\u0434\u0430\u0447 \u0433\u043b\u043e\u0431\u0430\u043b\u044c\u043d\u043e\u0439 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438<\/a>. \u0421\u0443\u043f\u0435\u0440\u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u043d\u044b\u0435 \u0434\u043d\u0438 \u0432 \u0420\u043e\u0441\u0441\u0438\u0438: \u0422\u0440\u0443\u0434\u044b \u043c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u043e\u0439 \u043a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u0438 (25-26 \u0441\u0435\u043d\u0442\u044f\u0431\u0440\u044f 2017 \u0433., \u0433. \u041c\u043e\u0441\u043a\u0432\u0430). \u0421. 671-682. 2017<\/li>\n
        17. \u0411\u0430\u0440\u043a\u0430\u043b\u043e\u0432 \u041a.\u0410., \u0413\u0435\u0442\u043c\u0430\u043d\u0441\u043a\u0430\u044f \u0410.\u0410., \u0418\u0441\u0440\u0430\u0444\u0438\u043b\u043e\u0432 \u0420.\u0410. \u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0430\u0434\u0430\u043f\u0442\u0438\u0432\u043d\u043e\u0439 \u0441\u0445\u0435\u043c\u044b \u0440\u0435\u0434\u0443\u043a\u0446\u0438\u0438 \u0440\u0430\u0437\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u0438 \u0434\u043b\u044f \u0437\u0430\u0434\u0430\u0447 \u043c\u043d\u043e\u0433\u043e\u044d\u043a\u0441\u0442\u0440\u0435\u043c\u0430\u043b\u044c\u043d\u043e\u0439 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u0441 \u043d\u0435\u043b\u0438\u043d\u0435\u0439\u043d\u044b\u043c\u0438 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u044f\u043c\u0438. \u0412\u0435\u0441\u0442\u043d\u0438\u043a \u0412\u0413\u0410\u0412\u0422, 2018 (\u043f\u0440\u0438\u043d\u044f\u0442\u0430 \u043a \u043f\u0435\u0447\u0430\u0442\u0438)<\/li>\n<\/ol>\n

           <\/p>\n

           <\/p>\n","protected":false},"excerpt":{"rendered":"

          \u041f\u043e \u043f\u0435\u0440\u0432\u043e\u043c\u0443 \u044d\u0442\u0430\u043f\u0443 \u043f\u0440\u043e\u0435\u043a\u0442\u0430 Andrey Pepelyshev and Anatoly Zhigljavsky. SSA analysis and forecasting of records for Earth temperature and ice extents. Statistics and Its Interface. 10, No.1, pp. 151-163, 2017. DOI: 10.4310\/SII.2017.v10.n1.a14 Accession Number WoS: 000386413100015 Scopus EID: 2-s2.0-85008219560 Andrey Pepelyshev, Yuri … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":40,"featured_media":0,"parent":4122,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"ngg_post_thumbnail":0},"_links":{"self":[{"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/pages\/4333"}],"collection":[{"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/users\/40"}],"replies":[{"embeddable":true,"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/comments?post=4333"}],"version-history":[{"count":2,"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/pages\/4333\/revisions"}],"predecessor-version":[{"id":4623,"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/pages\/4333\/revisions\/4623"}],"up":[{"embeddable":true,"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/pages\/4122"}],"wp:attachment":[{"href":"https:\/\/hpc-education.unn.ru\/en\/wp-json\/wp\/v2\/media?parent=4333"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}