Development and optimization of randomized functional numerical methods for solving the practically significant Fredholm integral equations of the second kind
Sibirskij žurnal industrialʹnoj matematiki, Tome 21 (2018) no. 2, pp. 32-45.

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Under study are the randomized algorithms for numerical solution of the Fredholm integral equations of the second kind (from the viewpoint of their application for the practically important problems of mathematical physics) are studied. The projection, grid and projection-grid methods are distinguished. Certain advantages of the projection and projection-grid methods are demonstrated (allowing using them for numerical solution of the equations with the integrable singularities in kernels and free terms).
Keywords: applied Fredholm integral equations of the second kind, integrable singularity, numerical randomized functional method, projection, grid, projection-grid functional algorithm.
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A. V. Voytishek. Development and optimization of randomized functional numerical methods for solving the practically significant Fredholm integral equations of the second kind. Sibirskij žurnal industrialʹnoj matematiki, Tome 21 (2018) no. 2, pp. 32-45. http://geodesic.mathdoc.fr/item/SJIM_2018_21_2_a2/

[1] Mikhailov G. A., Voitishek A. V., Chislennoe statisticheskoe modelirovanie. Metody Monte-Karlo, Akademiya, M., 2006

[2] Kantorovich L. V., Akilov G. P., Funktsionalnyi analiz, Nauka, M., 1984 | MR

[3] Voitishek A. V., Funktsionalnye otsenki metoda Monte-Karlo, izd. NGU, Novosibirsk, 2007

[4] Borovkov A. A., Teoriya veroyatnostei, Nauka, M., 1986 | MR

[5] Bakhvalov N. S., Chislennye metody, Nauka, M., 1975 | MR

[6] Frolov A. S., Chentsov N. N., “Ispolzovanie zavisimykh ispytanii v metode Monte-Karlo dlya polucheniya gladkikh krivykh”, Tr. Vsesoyuz. soveschaniya po teorii veroyatnostei i matematicheskoi statistike, Vilnyus, 1962, 425–437 | Zbl

[7] Mikhailov G. A., Tracheva N. V., Ukhinov S. A., “Randomizirovannyi proektsionnyi metod dlya otsenki uglovykh raspredelenii polyarizovannogo izlucheniya na osnove chislennogo statisticheskogo modelirovaniya”, Zhurn. vychisl. matematiki i mat. fiziki, 56:9 (2016), 1560–1570 | DOI | Zbl

[8] Rogazinskii S. V., “Statisticheskoe modelirovanie na osnove proektsionnogo metoda dlya nelineinogo uravneniya Boltsmana”, Tez. konf. “Marchukovskie chteniya–2017”, Novosibirsk, 2017, 82

[9] Mikhailov G. A., Optimizatsiya vesovykh metodov Monte-Karlo, Nauka, M., 1987 | MR

[10] Mikhailov G. A., Vesovye metody Monte-Karlo, Izd-vo SO RAN, Novosibirsk, 2000 | MR

[11] Marchuk G. A., Agoshkov V. A., Vvedenie v proektsionno-setochnye metody, Nauka, M., 1981 | MR

[12] Shkarupa E. V., Voytishek A. V., “Convergence of discrete-stochastic numerical procedures with independent or weakly dependent estimators at grid nodes”, J. Stat. Plan. Inference, 85 (2000), 199–211 | DOI | MR | Zbl

[13] Ramazanov M. D., Rakhmatullin D. Ya., Valeeva L. Z., Bannikova E. L., “Reshenie integralnykh uravnenii na mnogoprotsessornykh vychislitelnykh sistemakh”, Zhurn. SFU. Tekhnika i tekhnologii, 2:1 (2009), 69–87

[14] Bulgakova T. E., Voitishek A. V., “Uslovnaya optimizatsiya randomizirovannogo iteratsionnogo metoda”, Zhurn. vychisl. matematiki i mat. fiziki, 49:7 (2009), 1148–1157 | MR | Zbl

[15] Ivanov V. M., Kulchitskii O. Yu., “Metod chislennogo resheniya integralnykh uravnenii na sluchainoi setke”, Differents. uravneniya, 26:2 (1990), 333–341 | MR | Zbl

[16] Berkovskii N. A., Modernizatsiya polustatisticheskogo metoda chislennogo resheniya integralnykh uravnenii, Dis. $\dots$ kand. fiz.-mat. nauk, SPb., 2006

[17] Vladimirov V. S., Uravneniya matematicheskoi fiziki, Nauka, M., 1981 | MR

[18] Sabelfeld K. K., Statisticheskoe modelirovaniya v zadachakh matematicheskoi fiziki, izd. NGU, Novosibirsk, 1992 | MR