RUS  ENG
Full version
PEOPLE

Kel'manov Alexander Vasiljevich

Publications in Math-Net.Ru

  1. Recognition of a quasi-periodic sequence containing an unknown number of nonlinearly extended reference subsequences

    Zh. Vychisl. Mat. Mat. Fiz., 61:7 (2021),  1162–1171
  2. The minimization problem for the sum of weighted convolution differences: the case of a given number of elements in the sum

    Sib. Zh. Vychisl. Mat., 23:2 (2020),  127–142
  3. Problem of minimizing a sum of differences of weighted convolutions

    Zh. Vychisl. Mat. Mat. Fiz., 60:12 (2020),  2015–2027
  4. Complexity of some problems of quadratic partitioning of a finite set of points in Euclidean space into balanced clusters

    Zh. Vychisl. Mat. Mat. Fiz., 60:1 (2020),  151–158
  5. Exact algorithms of searching for the largest size cluster in two integer 2-clustering problems

    Sib. Zh. Vychisl. Mat., 22:2 (2019),  121–136
  6. Quadratic Euclidean 1-Mean and 1-Median 2-Clustering Problem with Constraints on the Size of the Clusters: Complexity and Approximability

    Trudy Inst. Mat. i Mekh. UrO RAN, 25:4 (2019),  69–78
  7. Polynomial-time solvability of the one-dimensional case of an NP-hard clustering problem

    Zh. Vychisl. Mat. Mat. Fiz., 59:9 (2019),  1617–1625
  8. Randomized algorithms for some hard-to-solve problems of clustering a finite set of points in Euclidean space

    Zh. Vychisl. Mat. Mat. Fiz., 59:5 (2019),  895–904
  9. On the Complexity of Some Max–Min Clustering Problems

    Trudy Inst. Mat. i Mekh. UrO RAN, 24:4 (2018),  189–198
  10. A randomized algorithm for a sequence 2-clustering problem

    Zh. Vychisl. Mat. Mat. Fiz., 58:12 (2018),  2169–2178
  11. Np-hardness of some Euclidean problems of partitioning a finite set of points

    Zh. Vychisl. Mat. Mat. Fiz., 58:5 (2018),  852–856
  12. Polynomial-time approximation algorithm for the problem of cardinality-weighted variance-based 2-clustering with a given center

    Zh. Vychisl. Mat. Mat. Fiz., 58:1 (2018),  136–142
  13. Exact pseudopolynomial algorithm for one sequence partitioning problem

    Avtomat. i Telemekh., 2017, no. 1,  80–90
  14. An approximation scheme for a problem of finding a subsequence

    Sib. Zh. Vychisl. Mat., 20:4 (2017),  379–392
  15. On pseudopolynomial-time solvability of a quadratic Euclidean problem of finding a family of disjoint subsets

    Sib. Zh. Vychisl. Mat., 20:1 (2017),  15–22
  16. Approximation scheme for the problem of weighted 2-partitioning with a fixed center of one cluster

    Trudy Inst. Mat. i Mekh. UrO RAN, 23:3 (2017),  159–170
  17. Approximation algorithm for the problem of partitioning a sequence into clusters

    Zh. Vychisl. Mat. Mat. Fiz., 57:8 (2017),  1392–1400
  18. Exact pseudopolinomial algorithms for a balanced $2$-clustering problem

    Diskretn. Anal. Issled. Oper., 23:3 (2016),  21–34
  19. Fully polynomial-time approximation scheme for a sequence $2$-clustering problem

    Diskretn. Anal. Issled. Oper., 23:2 (2016),  21–40
  20. An approximation algorithm for the problem of partitioning a sequence into clusters with constraints on their cardinalities

    Trudy Inst. Mat. i Mekh. UrO RAN, 22:3 (2016),  144–152
  21. On the complexity and approximability of some Euclidean optimal summing problems

    Zh. Vychisl. Mat. Mat. Fiz., 56:10 (2016),  1831–1836
  22. On the complexity of some quadratic Euclidean 2-clustering problems

    Zh. Vychisl. Mat. Mat. Fiz., 56:3 (2016),  498–504
  23. Fully polynomial-time approximation scheme for a special case of a quadratic Euclidean 2-clustering problem

    Zh. Vychisl. Mat. Mat. Fiz., 56:2 (2016),  332–340
  24. An exact pseudopolynomial algorithm for a bi-partitioning problem

    Diskretn. Anal. Issled. Oper., 22:4 (2015),  50–62
  25. Polynomial-time approximation scheme for a problem of partitioning a finite set into two clusters

    Trudy Inst. Mat. i Mekh. UrO RAN, 21:3 (2015),  100–109
  26. An approximation polynomial-time algorithm for a sequence bi-clustering problem

    Zh. Vychisl. Mat. Mat. Fiz., 55:6 (2015),  1076–1085
  27. A randomized algorithm for two-cluster partition of a set of vectors

    Zh. Vychisl. Mat. Mat. Fiz., 55:2 (2015),  335–344
  28. A $2$-approximate algorithm to solve one problem of the family of disjoint vector subsets

    Avtomat. i Telemekh., 2014, no. 4,  5–19
  29. Complexity of the Euclidean max cut problem

    Diskretn. Anal. Issled. Oper., 21:4 (2014),  3–11
  30. FPTAS for solving a problem of search for a vector subset

    Diskretn. Anal. Issled. Oper., 21:3 (2014),  41–52
  31. Approximation algorithm for one problem of partitioning a sequence

    Diskretn. Anal. Issled. Oper., 21:1 (2014),  53–66
  32. Efficient algorithms with performance estimates for some problems of finding several cliques in a complete undirected weighted graph

    Trudy Inst. Mat. i Mekh. UrO RAN, 20:2 (2014),  99–112
  33. A $2$-approximation polynomial algorithm for one clustering problem

    Diskretn. Anal. Issled. Oper., 20:4 (2013),  36–45
  34. On the complexity of some vector sequence clustering problems

    Diskretn. Anal. Issled. Oper., 20:2 (2013),  47–57
  35. $2$-approximate algorithm for finding a clique with minimum weight of vertices and edges

    Trudy Inst. Mat. i Mekh. UrO RAN, 19:2 (2013),  134–143
  36. Recognition of a sequence as a structure containing series of recurring vectors from an alphabet

    Zh. Vychisl. Mat. Mat. Fiz., 53:7 (2013),  1212–1224
  37. Точные псевдополиномиальные алгоритмы для некоторых труднорешаемых задач поиска подпоследовательности векторов

    Zh. Vychisl. Mat. Mat. Fiz., 53:1 (2013),  143–153
  38. Pseudopolynomial algorithms for certain computationally hard vector subset and cluster analysis problems

    Avtomat. i Telemekh., 2012, no. 2,  156–162
  39. Approximation algorithms for some NP-hard problems of searching a vectors subsequence

    Diskretn. Anal. Issled. Oper., 19:3 (2012),  27–38
  40. О сложности некоторых задач выбора подпоследовательности векторов

    Zh. Vychisl. Mat. Mat. Fiz., 52:12 (2012),  2284–2291
  41. An approximation algorithm for one problem of cluster analysis

    Diskretn. Anal. Issled. Oper., 18:2 (2011),  29–40
  42. The approximation algorithm for one problem of searching for subset of vectors

    Diskretn. Anal. Issled. Oper., 18:1 (2011),  61–69
  43. On the complexity of some cluster analysis problems

    Zh. Vychisl. Mat. Mat. Fiz., 51:11 (2011),  2106–2112
  44. NP-completeness of some problems of a vectors subset choice

    Diskretn. Anal. Issled. Oper., 17:5 (2010),  37–45
  45. On the issue of algorithmic complexity of one cluster analysis problem

    Diskretn. Anal. Issled. Oper., 17:2 (2010),  39–45
  46. The $NP$-completeness of some problems of searching for vector subsets

    Trudy Inst. Mat. i Mekh. UrO RAN, 16:3 (2010),  121–129
  47. On the complexity of some data analysis problems

    Zh. Vychisl. Mat. Mat. Fiz., 50:11 (2010),  2045–2051
  48. On one problem of searching for tuples of fragments in a numerical sequence

    Diskretn. Anal. Issled. Oper., 16:4 (2009),  31–46
  49. On one recognition problem of vector alphabet generating a sequence with a quasi-periodical structure

    Sib. Zh. Vychisl. Mat., 12:3 (2009),  275–287
  50. Complexity of certain problems of searching for subsets of vectors and cluster analysis

    Zh. Vychisl. Mat. Mat. Fiz., 49:11 (2009),  2059–2065
  51. On one variant of the vectors subset choice problem

    Diskretn. Anal. Issled. Oper., 15:5 (2008),  20–34
  52. Распознавание квазипериодической последовательности, включающей повторяющийся набор фрагментов

    Sib. Zh. Ind. Mat., 11:2 (2008),  74–87
  53. Optimal detection of a recurring tuple of reference fragments in a quasi-periodic sequence

    Sib. Zh. Vychisl. Mat., 11:3 (2008),  311–327
  54. Off-line detection of a quasi-periodically recurring fragment in a numerical sequence

    Trudy Inst. Mat. i Mekh. UrO RAN, 14:2 (2008),  81–88
  55. A posteriori joint detection of a recurring tuple of reference fragments in a quasi-periodic sequence

    Zh. Vychisl. Mat. Mat. Fiz., 48:12 (2008),  2247–2260
  56. A posteriori joint detection of reference fragments in a quasi-periodic sequence

    Zh. Vychisl. Mat. Mat. Fiz., 48:5 (2008),  899–915
  57. Recognition of a numerical sequence that includes series of quasiperiodically repeating standard fragments

    Sib. Zh. Ind. Mat., 10:4 (2007),  61–75
  58. Optimal detection of a given number of unknown quasiperiodic fragments in a numerical sequence

    Sib. Zh. Vychisl. Mat., 10:2 (2007),  159–175
  59. A posteriori detection of a given number of unknown quasiperiodic fragments in a numerical sequence

    Sib. Zh. Ind. Mat., 9:3 (2006),  50–65
  60. Joint a posteriori detection and identification of quasiperiodic fragments in a sequence from pieces of them

    Sib. Zh. Ind. Mat., 9:2 (2006),  55–74
  61. A posteriori detection of a quasiperiodic fragment with a given number of repetitions in a numerical sequence

    Sib. Zh. Ind. Mat., 9:1 (2006),  55–74
  62. Joint detection of a given number of reference fragments in a quasi-periodic sequence and its partition into segments containing series of identical fragments

    Zh. Vychisl. Mat. Mat. Fiz., 46:1 (2006),  172–189
  63. Recognition of a numerical sequence that includes series of quasiperiodic repeating standard fragments. The case of a known number of fragments

    Sib. Zh. Ind. Mat., 8:3 (2005),  69–86
  64. Joint a posteriori detection and identification of a given number of quasiperiodic fragments in a sequence from pieces of them

    Sib. Zh. Ind. Mat., 8:2 (2005),  83–102
  65. Simultaneous detection in a quasiperiodic sequence of a given number of fragments from a standard set and its partition into sections that include series of identical fragments

    Sib. Zh. Ind. Mat., 7:4 (2004),  71–91
  66. Recognition of a numerical sequence from fragments of a quasiperiodically repeating standard sequence

    Sib. Zh. Ind. Mat., 7:2 (2004),  68–87
  67. A posteriori detection of a quasiperiodically repeating fragment of a numerical sequence under conditions of noise and data loss

    Sib. Zh. Ind. Mat., 6:2 (2003),  46–63
  68. Recognition of a quasiperiodic sequence that includes identical subsequences-fragments

    Sib. Zh. Ind. Mat., 5:4 (2002),  38–54
  69. A posteriori detection of identical subsequence-fragments in a quasiperiodic sequence

    Sib. Zh. Ind. Mat., 5:2 (2002),  94–108
  70. Recognition of a quasiperiodic sequence formed from a given number of truncated subsequences

    Sib. Zh. Ind. Mat., 5:1 (2002),  85–104
  71. Posterior detection of a given number of identical subsequences in a quasi-periodic sequence

    Zh. Vychisl. Mat. Mat. Fiz., 41:5 (2001),  807–820
  72. A posteriori joint detection and distinguishing of subsequences in a quasiperiodic sequence

    Sib. Zh. Ind. Mat., 3:2 (2000),  115–139
  73. A posteriori detection of a given number of truncated subsequences in a quasiperiodic sequence

    Sib. Zh. Ind. Mat., 3:1 (2000),  137–156
  74. The recognition error probability bounds for quasi-periodic sequence formed from given number of identical subsequences

    Sib. Zh. Vychisl. Mat., 3:4 (2000),  333–344
  75. A posteriori joint detection and distinction of a given number of subsequences in a quasiperiodic sequence

    Sib. Zh. Ind. Mat., 2:2 (1999),  106–119
  76. Recognition of a quasiperiodic sequence formed from a given number of identical subsequences

    Sib. Zh. Ind. Mat., 2:1 (1999),  53–74
  77. Optimal detection of given number of identical subsequences in quasiperiodic sequence

    Sib. Zh. Vychisl. Mat., 2:4 (1999),  333–349
  78. A lower bound for the error probability of recognizing a quasiperiodic sequence of pulses that is distorted by Gaussian uncorrelated noise

    Sib. Zh. Ind. Mat., 1:2 (1998),  113–126

© Steklov Math. Inst. of RAS, 2026