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Verjetnostna metoda : delo diplomskega seminarja
ID Stibilj, Timotej (Author), ID Perman, Mihael (Mentor) More about this mentor... This link opens in a new window

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Abstract
Verjetnostno metodo uporabljamo za nekostrukcijsko dokazovanje obstaja kombinatoričnih objektov z določenimi lastnostmi. Spoznamo osnove verjetnostnih algoritmov in povezavo z verjetnostno metodo; dokaz z verjetnostno metodo lahko pogosto prevedemo na verjetnostni algoritem. Spoznamo več načinov uporabe verjetnostne metode: osnovno metodo, metodo izbrisa, uporabo linearnosti pričakovane vrednosti in metodo drugega momenta. Pri vsaki metodi je predstavljen najmanj en primer. Predstavljena so Ramseyeva števila, barvanje hipergrafov in turnirji. Na primeru maksimalnega prereza grafa prikažemo prevedbo dokaza na verjetnostni algoritem in njegovo derandomizacijo. Dokažemo Erdősev izrek, ki pravi, da obstaja graf s poljubno veliko ožino in poljubno velikim kromatičnim številom. Spoznamo pojem slučajnega grafa in pragovne funkcije ter poiščemo pragovno funkcijo za vsebovanost danega podgrafa.

Language:Slovenian
Keywords:verjetnostna metoda, verjetnostni algoritem, metoda izbrisa, pričakovana vrednost, neenakost Markova, neenakost Čebiševa, slučajni graf
Work type:Final seminar paper
Typology:2.11 - Undergraduate Thesis
Organization:FMF - Faculty of Mathematics and Physics
Year:2024
PID:20.500.12556/RUL-159615 This link opens in a new window
UDC:519.17
COBISS.SI-ID:201843971 This link opens in a new window
Publication date in RUL:14.07.2024
Views:310
Downloads:57
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Secondary language

Language:English
Title:Probabilistic method
Abstract:
The probabilistic method is used for nonconstructive proofs of existence of combinatorial objects with certain properties. We present the basic ideas of randomized algorithms and show their connection to the probabilistic method. Proofs using the probabilistic method often lead to randomized algorithms for such objects. Several ways of using the probabilistic method are shown, including the basic method, alteration, the use of linearity of expectation and the second-moment method. There is at least one example for each method. We present Ramsey numbers, hypergraph coloring and tournaments. In the example of the maximum cut problem, we present the randomized algorithm and its derandomization. We prove Erdős’ theorem, which states that there exists a graph with arbitrarily large girth and arbitrarily large chromatic number. Lastly, we introduce the idea of random graphs, their threshold functions and find the threshold function for containing a given subgraph.

Keywords:probabilistic method, randomized algorithm, alteration, expected value, Markov’s inequality, Chebyshev’s inequality, random graph

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