Kombinatorā optimizācija ar dziļajiem neironu tīkliem

6533b85dfe1ef96bd12becae

RESEARCH PRODUCT

Kombinatorā optimizācija ar dziļajiem neironu tīkliem

Emīls Ozoliņš

subject

Jauktu veselu skaitļu lineārā programmēšana Datorzinātne Grafu neironu tīkli Būla formulas apmierināšana Nepārraudzītā mašīnmācīšanās

description

Kombinatorās optimizācijas problēmām ir liela praktiskā nozīme, tomēr tās, parasti, ir NP-grūtas un to risināšana ir apgrūtināta. Darba mērķis ir noskaidrot, vai dziļo neironu tīklu metodes var tikt pielietotas kombinatorai optimizācijai. Kā reprezentatīvas problēmas tiek izvēlētas Būla formulas apmierināšana (SAT) un jauktu veselu skaitļu programmēšana (MILP). Abām problēmām tiek izveidotas specializētas ziņu pārsūtīšanas grafu neironu tīklu arhitektūras, kas papildinātas ar autora piedāvātu vaicājumu mehānismu. Tīklu apmācība tiek veikta ar darbā piedāvātām nepārraudzītās mašīnmācīšanās zaudējumu funkcijām (nav nepieciešams risinājumu). Piedāvātais neironu tīklu risinātājs SAT problēmai pārspēj NeuroCore neironu tīklu arhitektūru uz 3-SAT, grafa krāsošanas un SHA-1 pirmtēla uzbrukuma uzdevumiem. Savukārt, piedāvātais neironu tīklu MILP risinātājs pārspēj Sudoku neironu tīklu risinātājus un tiek pārbaudīts uz mugursomas problēmas. Abas iegūtās arhitektūras tiek salīdzinātas arī ar klasiskajiem algoritmiem.

year	journal	country	edition	language
2022-01-01

https://dspace.lu.lv/dspace/handle/7/59989

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